Multiome analysis
https://satijalab.org/signac/articles/merging.html https://satijalab.org/signac/articles/integrate_atac.html https://satijalab.org/signac/articles/pbmc_multiomic.html
merge peaks from all samples
recount
merge
(if necessary) integrate
motif enrichment analysis
TF footprint
load RNA-seq
match cell barcodes - actually, cellranger-arc gives to GEX and ATAC the GEX barcodes
do integrated processing
- joint UMAP - done with WNN integration
- gene-peak connection
- multiome GRN
- MOFA 2
co-accessibility analysis - not done, requires Monocle 3
General Setup
Setup chunk
Setup reticulate
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")[1] "/nas/groups/treutlein/USERS/tomasgomes/projects/pallium_evo"Load libraries
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)[1] FALSEuse_python(Sys.which("python"))
py_config()python: /home/tpires/bin/miniconda3/bin/python
libpython: /home/tpires/bin/miniconda3/lib/libpython3.8.so
pythonhome: /home/tpires/bin/miniconda3:/home/tpires/bin/miniconda3
version: 3.8.3 (default, May 19 2020, 18:47:26) [GCC 7.3.0]
numpy: /home/tpires/bin/miniconda3/lib/python3.8/site-packages/numpy
numpy_version: 1.18.5
NOTE: Python version was forced by RETICULATE_PYTHONscATAC
Gene setup
Mitochondrial genes
library(Pando)
library(foreach) # needed for Pando
library(igraph) # for Pando network analysis
# multiome factor analysis
library(MOFA2)
Attaching package: ‘MOFA2’
The following object is masked from ‘package:stats’:
predictsource("scripts/MOFA_toSeurat.R") #might not be needed with MOFA2 dev version
# getting GTF
# remotes::install_github("alyssafrazee/ballgown")
library(ballgown)
Attaching package: ‘ballgown’
The following object is masked from ‘package:data.table’:
last
The following object is masked from ‘package:ggplot2’:
expr
The following object is masked from ‘package:base’:
structure# plotting
library(ggplot2)
library(ggnetwork)
library(ggrepel)
library(patchwork)Read in axolotl features
mtgenes = c("COX1", "COX2", "COX3", "ATP6", "ND1", "ND5", "CYTB", "ND2", "ND4",
"ATP8", "MT-CO1", "COI", "LOC9829747")
mtgenes = c(mtgenes, paste0("MT", mtgenes), paste0("MT-", mtgenes))Load data
CellRanger peaks
Read in data
gtf_axolotl = "/local1/USERS/tomasgomes/multiome_analysis/AmexT_v47.FULL_corr_chr_cut.gtf"
granges_axolotl = ballgown::gffReadGR(gtf_axolotl)connection is not positioned at the start of the file, rewinding it# adding a gene biotype, as that's necessary for TSS metaprofile
granges_axolotl$gene_biotype = "protein_coding"
# with need to add the "proper" gene name
basedir = "/links/groups/treutlein/USERS/tomasgomes/gene_refs/axolotl/Amex_T_v47/"
gene_name_match = read.table(paste0(basedir, "AmexT_v47.FULL_t2g_note.txt"), sep = "\t")[,2:3]
gene_name_match = gene_name_match[!duplicated(gene_name_match$V2), ]
rownames(gene_name_match) = gene_name_match$V2
newgenenames = gene_name_match[granges_axolotl$gene_id,2]
granges_axolotl$gene_name = newgenenamesMerge peaks
samples = c("a1_1", "a1_2", "a3_1", "a3_2")
#data_dir = "/links/groups/treutlein/USERS/tomasgomes/data/axolotl/"
data_dir = "/local1/USERS/tomasgomes/multiome_analysis/"
counts_l = list()
frags_l = list()
peaks_l = list()
for(s in samples){
counts_l[[s]] = Read10X_h5(filename = paste0(data_dir, s, "_ARC/outs/filtered_feature_bc_matrix.h5"))
frags_file = paste0(data_dir, s, "_ARC/outs/atac_fragments.tsv.gz")
peaks = read.table(paste0(data_dir, s, "_ARC/outs/atac_peaks.bed"),
col.names = c("chr", "start", "end"))
peaks_l[[s]] = makeGRangesFromDataFrame(peaks)
frags_l[[s]] = CreateFragmentObject(path = frags_file, cells = colnames(counts_l[[s]]$Peaks))
}'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 7341 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 9035 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 7835 cell barcodes
'giveCsparse' has been deprecated; setting 'repr = "T"' for youGenome matrix has multiple modalities, returning a list of matrices for this genome
Computing hash
Checking for 8060 cell barcodesMake all individual Seurats with common peaks
# Create a unified set of peaks to quantify in each dataset
combined.peaks = reduce(x = c(peaks_l$a1_1, peaks_l$a1_2, peaks_l$a3_1, peaks_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined.peaks)
combined.peaks = combined.peaks[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined.peaks = restrict(combined.peaks, start = 1)
combined.peaksGRanges object with 631645 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1ps1 71686-72012 *
[2] chr1ps1 74070-74148 *
[3] chr1ps1 141663-141901 *
[4] chr1ps1 144157-144549 *
[5] chr1ps1 146038-147572 *
... ... ... ...
[631641] chr14q 436366888-436367192 *
[631642] chr14q 436386394-436387106 *
[631643] chr14q 436388915-436389695 *
[631644] chr14q 436390286-436390344 *
[631645] chr14q 436403784-436405280 *
-------
seqinfo: 66 sequences from an unspecified genome; no seqlengthsGetting MT peaks and calculating MT%
srat_atac_l = list()
for(s in names(frags_l)){
feat = FeatureMatrix(fragments = frags_l[[s]], features = combined.peaks,
cells = colnames(counts_l[[s]]$Peaks))
ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
min.features = 100, annotation = granges_axolotl)
srat_atac_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
srat_atac_l[[s]]$dataset = s
srat_atac_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}Extracting reads overlapping genomic regions
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=33m 53sExtracting reads overlapping genomic regions
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tss_atac_plts = list()
frag_atac_plts = list()
for(n in names(srat_atac_l)){
# compute nucleosome signal score per cell
srat_atac_l[[n]] = NucleosomeSignal(object = srat_atac_l[[n]])
# compute TSS enrichment score per cell
srat_atac_l[[n]] = TSSEnrichment(object = srat_atac_l[[n]], fast = FALSE)
srat_atac_l[[n]]$high.tss = ifelse(srat_atac_l[[n]]$TSS.enrichment > 2, 'High', 'Low')
tss_atac_plts[[n]] = TSSPlot(srat_atac_l[[n]], group.by = 'high.tss') + NoLegend()
srat_atac_l[[n]]$nucleosome_group = ifelse(srat_atac_l[[n]]$nucleosome_signal > 4, 'NS > 2', 'NS < 4')
# this is veeery slow (~30-40min)
frag_atac_plts[[n]] = FragmentHistogram(object = srat_atac_l[[n]], group.by = 'nucleosome_group')
}Save data
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l_raw.RDS")MACS peaks
Peak calling with MACS
macs_l = list()
for(n in names(srat_atac_l)){
print(n)
srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = 5)
srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
cordat = DepthCor(srat_atac_l[[n]], n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
print(dims_use)
srat_atac_l[[n]] = FindNeighbors(object = srat_atac_l[[n]], reduction = 'lsi', dims = dims_use,
force.recalc = T, graph.name = "thegraph")
srat_atac_l[[n]] = FindClusters(object = srat_atac_l[[n]], verbose = FALSE, algorithm = 3,
graph.name = "thegraph", resolution = 2)
macs_l[[n]] = CallPeaks(object = srat_atac_l[[n]], group.by = "thegraph_res.2",
macs2.path = "~/bin/miniconda3/envs/macs2env/bin/macs3",
effective.genome.size = 2.0e+10, verbose = F)
}How do new peaks compare with the original?
# Create a unified set of peaks to quantify in each dataset
combined_macs = reduce(x = c(macs_l$a1_1, macs_l$a1_2, macs_l$a3_1, macs_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined_macs)
combined_macs = combined_macs[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined_macs = restrict(combined_macs, start = 1)
combined_macs
boxplot(x = list("MACS" = log10(width(combined_macs)), "Original" = log10(width(combined.peaks))))Generate new counts and objects
# Create a unified set of peaks to quantify in each dataset
combined_macs = reduce(x = c(macs_l$a1_1, macs_l$a1_2, macs_l$a3_1, macs_l$a3_2))
# Filter out bad peaks based on length
peakwidths = width(combined_macs)
combined_macs = combined_macs[peakwidths > 12]
# there is a problem with coordinates starting at 0 for some reason...
combined_macs = restrict(combined_macs, start = 1)
combined_macsGRanges object with 135606 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1ps1 186559-186766 *
[2] chr1ps1 276267-276727 *
[3] chr1ps1 276830-277150 *
[4] chr1ps1 2111397-2111909 *
[5] chr1ps1 2286644-2286846 *
... ... ... ...
[135602] chr14q 436116542-436117020 *
[135603] chr14q 436167769-436168171 *
[135604] chr14q 436386504-436386747 *
[135605] chr14q 436389008-436389318 *
[135606] chr14q 436403902-436404556 *
-------
seqinfo: 66 sequences from an unspecified genome; no seqlengthsboxplot(x = list("MACS" = log10(width(combined_macs)), "Original" = log10(width(combined.peaks))))
Getting MT peaks and calculating MT%
srat_macs_l = list()
for(s in names(frags_l)){
print(s)
feat = FeatureMatrix(fragments = frags_l[[s]], features = combined_macs,
cells = colnames(counts_l[[s]]$Peaks))
ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
min.features = 100, annotation = granges_axolotl)
srat_macs_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
srat_macs_l[[s]]$dataset = s
srat_macs_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}[1] "a1_1"Extracting reads overlapping genomic regions
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[1] "a1_2"Extracting reads overlapping genomic regions
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[1] "a3_1"Extracting reads overlapping genomic regions
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 16s
[1] "a3_2"Extracting reads overlapping genomic regions
| | 0 % ~calculating
|+ | 1 % ~08m 55s
|++ | 3 % ~08m 11s
|+++ | 4 % ~08m 03s
|+++ | 6 % ~08m 13s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08m 50sNucleosome and TSS signal (serve as QC metrics)
# match regions with MT genes
allregions = rownames(srat_macs_l$a1_1)
ischrmt = lapply(chrmt, function(x) grepl(x, allregions))
ischrmt = Reduce(cbind, ischrmt)
ischrmt = rowSums(ischrmt)>0
for(n in names(srat_macs_l)){
all_cl = ClosestFeature(srat_macs_l[[n]], regions = allregions[ischrmt])
## get MT regions
mt_peaks = all_cl[all_cl$gene_id %in% mtid,"query_region"]
srat_macs_l[[n]] = PercentageFeatureSet(srat_macs_l[[n]], features = mt_peaks, col.name = "mt_perc")
}Save data
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l_raw.RDS")Quality Control
Plotting QC metrics
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l_raw.RDS")Removing cells based on QC metrics
pdf("results/multiome/vln_qc_ATAC.pdf", width = 14, height = 4)
vln_atac_plts = list()
for(n in names(srat_atac_l)){
vln_atac_plts[[n]] = VlnPlot(object = srat_atac_l[[n]], pt.size = 0.1, ncol = 5, log = T,
features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
'TSS.enrichment', 'nucleosome_signal'))
print(vln_atac_plts[[n]])
}
dev.off()null device
1 pdf("results/multiome/vln_qc_MACS.pdf", width = 14, height = 4)vln_macs_plts = list()
for(n in names(srat_macs_l)){
vln_macs_plts[[n]] = VlnPlot(object = srat_macs_l[[n]], pt.size = 0.1, ncol = 5, log = T,
features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
'TSS.enrichment', 'nucleosome_signal'))
print(vln_macs_plts[[n]])
}
dev.off()null device
1 Combine and analyse
Combine all ATAC data
# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2),
c(12000, 300, 12000, 300, 2, 3, 2))
names(qc_list) = names(srat_atac_l)
for(n in names(srat_atac_l)){
srat_atac_l[[n]] = subset(x = srat_atac_l[[n]],
subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
mt_perc<qc_list[[n]][7])
}
srat_atac_l$a1_1
An object of class Seurat
631645 features across 6187 samples within 1 assay
Active assay: ATAC (631645 features, 504027 variable features)
1 dimensional reduction calculated: lsi
$a1_2
An object of class Seurat
631645 features across 7135 samples within 1 assay
Active assay: ATAC (631645 features, 474058 variable features)
1 dimensional reduction calculated: lsi
$a3_1
An object of class Seurat
631645 features across 6003 samples within 1 assay
Active assay: ATAC (631645 features, 417276 variable features)
1 dimensional reduction calculated: lsi
$a3_2
An object of class Seurat
631645 features across 6324 samples within 1 assay
Active assay: ATAC (631645 features, 425918 variable features)
1 dimensional reduction calculated: lsi# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5),
c(10000, 200, 10000, 200, 2, 3, 5))
names(qc_list) = names(srat_macs_l)
for(n in names(srat_macs_l)){
srat_macs_l[[n]] = subset(x = srat_macs_l[[n]],
subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
mt_perc<qc_list[[n]][7])
}
srat_macs_l$a1_1
An object of class Seurat
135606 features across 6408 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a1_2
An object of class Seurat
135606 features across 7308 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a3_1
An object of class Seurat
135606 features across 6011 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)
$a3_2
An object of class Seurat
135606 features across 6372 samples within 1 assay
Active assay: ATAC (135606 features, 0 variable features)Check how the data looks between samples
# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_atac_l)){
cn = unlist(lapply(strsplit(colnames(srat_atac_l[[n]]), "-", fixed = T), function(x) x[1]))
srat_atac_l[[n]] = RenameCells(srat_atac_l[[n]],
new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}
# merging
srat_atac_all = Reduce(merge, srat_atac_l)
# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_macs_l)){
cn = unlist(lapply(strsplit(colnames(srat_macs_l[[n]]), "-", fixed = T), function(x) x[1]))
srat_macs_l[[n]] = RenameCells(srat_macs_l[[n]],
new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}
# merging
srat_macs_all = Reduce(merge, srat_macs_l)Processing
Processing ATAC-seq data
CoveragePlot(object = srat_atac_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
CoveragePlot(object = srat_macs_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
Check correlation of LSI with sequencing depth (since often times the first component is technical var)
srat_atac_all = RunTFIDF(srat_atac_all)
Performing TF-IDF normalization
Some features contain 0 total countssrat_atac_all = FindTopFeatures(srat_atac_all, min.cutoff = 5)
srat_atac_all = RunSVD(srat_atac_all)Running SVD
Scaling cell embeddingssrat_macs_all = RunTFIDF(srat_macs_all)Performing TF-IDF normalization
Some features contain 0 total countssrat_macs_all = FindTopFeatures(srat_macs_all, min.cutoff = 5)
srat_macs_all = RunSVD(srat_macs_all)Running SVD
Scaling cell embeddingsGet UMAP
DepthCor(srat_atac_all, n = 30)
DepthCor(srat_macs_all, n = 30)
Integration
Processing for each ATAC-seq sample
cordat = DepthCor(srat_atac_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_atac_all = RunUMAP(object = srat_atac_all, reduction = 'lsi', dims = dims_use, verbose = F)The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionDimPlot(object = srat_atac_all, group.by = "dataset")
FeaturePlot(object = srat_atac_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
cordat = DepthCor(srat_macs_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_macs_all = RunUMAP(object = srat_macs_all, reduction = 'lsi', dims = dims_use, verbose = F)
DimPlot(object = srat_macs_all, group.by = "dataset")
FeaturePlot(object = srat_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Integrate based on each experiment - CellRanger peaks
for(n in names(srat_atac_l)){
srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = "q0")
srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
}Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddingsDepthCor(srat_atac_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_2, reduction = "lsi", n = 30)
for(n in names(srat_macs_l)){
srat_macs_l[[n]] = RunTFIDF(srat_macs_l[[n]])
srat_macs_l[[n]] = FindTopFeatures(srat_macs_l[[n]], min.cutoff = "q0")
srat_macs_l[[n]] = RunSVD(srat_macs_l[[n]])
}Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddingsDepthCor(srat_macs_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_2, reduction = "lsi", n = 30)
Integrate based on each experiment - MACS3 peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_l, reduction = "rlsi", dims = 2:30,
anchor.features = rownames(srat_atac_all))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++ | 25% ~06s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Finding all pairwise anchors
| | 0 % ~calculating No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 1383 anchors
|+++++++++ | 17% ~03m 49s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 516 anchors
|+++++++++++++++++ | 33% ~02m 51s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 460 anchors
|+++++++++++++++++++++++++ | 50% ~02m 05s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 639 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~01m 27s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 536 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~44s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 655 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 20s# integrate LSI embeddings
integ_all = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 3 into 2 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(integ_all, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_all = RunUMAP(integ_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_all, group.by = "dataset")
FeaturePlot(object = integ_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Processing ATAC-seq by animal
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_l, reduction = "rlsi", dims = 2:30,
anchor.features = rownames(srat_macs_all))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++ | 25% ~06s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Finding all pairwise anchors
| | 0 % ~calculating No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 1011 anchors
|+++++++++ | 17% ~01m 48s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 436 anchors
|+++++++++++++++++ | 33% ~01m 26s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 359 anchors
|+++++++++++++++++++++++++ | 50% ~01m 01s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 550 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~42s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 436 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~21s No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 475 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 04s# integrate LSI embeddings
integ_macs_all = IntegrateEmbeddings(anchorset = integration.anchors,
reductions = srat_macs_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 3 into 2 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_all = RunUMAP(integ_macs_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_all, group.by = "dataset")
FeaturePlot(object = integ_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Integrate based on each animal - CellRanger peaks
srat_atac_animal = SplitObject(srat_atac_all, split.by = "animal")
for(n in names(srat_atac_animal)){
srat_atac_animal[[n]] = RunTFIDF(srat_atac_animal[[n]])
srat_atac_animal[[n]] = FindTopFeatures(srat_atac_animal[[n]], min.cutoff = "q0")
srat_atac_animal[[n]] = RunSVD(srat_atac_animal[[n]])
}Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddingsDepthCor(srat_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_atac_animal$a3, reduction = "lsi", n = 30)
srat_macs_animal = SplitObject(srat_macs_all, split.by = "animal")
for(n in names(srat_macs_animal)){
srat_macs_animal[[n]] = RunTFIDF(srat_macs_animal[[n]])
srat_macs_animal[[n]] = FindTopFeatures(srat_macs_animal[[n]], min.cutoff = "q0")
srat_macs_animal[[n]] = RunSVD(srat_macs_animal[[n]])
}Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddings
Performing TF-IDF normalization
Some features contain 0 total countsRunning SVD
Scaling cell embeddingsDepthCor(srat_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_macs_animal$a3, reduction = "lsi", n = 30)
Integrate based on each animal - MACS3 peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(srat_atac_all))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s Finding all pairwise anchors
| | 0 % ~calculating No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 1294 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 44s# integrate LSI embeddings
integ_animal = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(integ_animal, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_animal = RunUMAP(integ_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_animal, group.by = "dataset")
FeaturePlot(object = integ_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Saving data so far
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(srat_macs_all))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Finding all pairwise anchors
| | 0 % ~calculating No filtering performed if passing to data rather than countsProjecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoodsFinding anchors
Found 1101 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=48s # integrate LSI embeddings
integ_macs_animal = IntegrateEmbeddings(anchorset = integration.anchors,
reductions = srat_macs_all[["lsi"]],
new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = RunUMAP(integ_macs_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_animal, group.by = "dataset")
FeaturePlot(object = integ_macs_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Clustering and annotation
We’re going with the integration by animal Find clusters
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l.RDS")
saveRDS(srat_atac_animal, file = "data/processed/multiome/srat_atac_animal.RDS")
saveRDS(srat_atac_all, file = "data/processed/multiome/srat_atac_all.RDS")
saveRDS(integ_all, file = "data/processed/multiome/integ_all.RDS")
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal.RDS")
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l.RDS")
saveRDS(srat_macs_animal, file = "data/processed/multiome/srat_macs_animal.RDS")
saveRDS(srat_macs_all, file = "data/processed/multiome/srat_macs_all.RDS")
saveRDS(integ_macs_all, file = "data/processed/multiome/integ_macs_all.RDS")Get DE peaks
DimPlot(object = integ_animal, label = TRUE) + NoLegend()
cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = FindNeighbors(object = integ_macs_animal, reduction = 'integrated_lsi',
dims = dims_use,
force.recalc = T, graph.name = "thegraph")
Computing nearest neighbor graphComputing SNN
Only one graph name supplied, storing nearest-neighbor graph onlyinteg_macs_animal = FindClusters(object = integ_macs_animal, verbose = FALSE, algorithm = 3,
graph.name = "thegraph", resolution = 0.8)
DimPlot(object = integ_macs_animal, label = TRUE) + NoLegend()
Annotate said peaks
reg = unique(allmk_ATAC$gene)
closest_allmk = ClosestFeature(integ_animal, regions = unique(reg))
allmk_ATAC = merge(allmk_ATAC, closest_allmk[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_ATAC, file = "results/multiome/allmk_ATAC.csv", col.names = T, row.names = F, quote = F)
reg = unique(allmk_macs$gene)
closest_allmk_macs = ClosestFeature(integ_macs_animal, regions = unique(reg))
allmk_macs = merge(allmk_macs[,-c(8:10)],
closest_allmk_macs[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_macs, file = "results/multiome/allmk_macs.csv", col.names = T, row.names = F, quote = F)Example coverage plot
reg = unique(allmk_ATAC$gene)
closest_allmk = ClosestFeature(integ_animal, regions = unique(reg))
allmk_ATAC = merge(allmk_ATAC, closest_allmk[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_ATAC, file = "results/multiome/allmk_ATAC.csv", col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredreg = unique(allmk_macs$gene)
closest_allmk_macs = ClosestFeature(integ_macs_animal, regions = unique(reg))
allmk_macs = merge(allmk_macs[,-c(8:10)],
closest_allmk_macs[,c("query_region","type","distance","gene_name")],
by.x = "gene", by.y = "query_region", all.x = T)
write.csv(allmk_macs, file = "results/multiome/allmk_macs.csv", col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredGene activities
Obtaining gene activities - helps give meaning to the detected peaks
CoveragePlot(object = integ_animal, region = "chr8ps1-430732645-430746183",
extend.upstream = 5000, extend.downstream = 5000)
CoveragePlot(object = integ_macs_animal, region = "chr8ps1-430732645-430746183",
extend.upstream = 5000, extend.downstream = 5000)
Plotting gene activities and get DE genes
gene_act = GeneActivity(integ_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)Extracting gene coordinates
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~12m 32s
|++++ | 7 % ~10m 26s
|++++++ | 10% ~09m 44s
|+++++++ | 14% ~09m 09s
|+++++++++ | 17% ~08m 51s
|+++++++++++ | 21% ~08m 25s
|+++++++++++++ | 24% ~08m 03s
|++++++++++++++ | 28% ~07m 32s
|++++++++++++++++ | 31% ~07m 06s
|++++++++++++++++++ | 34% ~06m 44s
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|+++++++++++++++++++++ | 41% ~05m 59s
|+++++++++++++++++++++++ | 45% ~05m 39s
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|++++++++++++++++++++++++++ | 52% ~04m 54s
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|++++++++++++++++++++++++++++++++ | 62% ~03m 51s
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|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 30s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 49sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 10s
|++++ | 7 % ~09m 12s
|++++++ | 10% ~09m 35s
|+++++++ | 14% ~09m 48s
|+++++++++ | 17% ~09m 30s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 33sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 14s
|++++ | 7 % ~08m 57s
|++++++ | 10% ~08m 37s
|+++++++ | 14% ~08m 33s
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|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 49s
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|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 45sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~12m 36s
|++++ | 7 % ~11m 33s
|++++++ | 10% ~11m 13s
|+++++++ | 14% ~10m 44s
|+++++++++ | 17% ~10m 28s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~47s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~23s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11m 18s# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)
# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_animal = NormalizeData(object = integ_animal, assay = 'GeneAct',
normalization.method = 'LogNormalize',
scale.factor = median(integ_animal$nCount_GeneAct))Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|gene_act = GeneActivity(integ_macs_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)Extracting gene coordinates
Extracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~13m 50s
|++++ | 7 % ~11m 07s
|++++++ | 10% ~10m 26s
|+++++++ | 14% ~09m 45s
|+++++++++ | 17% ~09m 11s
|+++++++++++ | 21% ~08m 48s
|+++++++++++++ | 24% ~08m 21s
|++++++++++++++ | 28% ~07m 48s
|++++++++++++++++ | 31% ~07m 24s
|++++++++++++++++++ | 34% ~06m 59s
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|+++++++++++++++++++++ | 41% ~06m 14s
|+++++++++++++++++++++++ | 45% ~05m 52s
|+++++++++++++++++++++++++ | 48% ~05m 30s
|++++++++++++++++++++++++++ | 52% ~05m 07s
|++++++++++++++++++++++++++++ | 55% ~04m 45s
|++++++++++++++++++++++++++++++ | 59% ~04m 23s
|++++++++++++++++++++++++++++++++ | 62% ~04m 00s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 39s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 16s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 55s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 27s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 29sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~09m 54s
|++++ | 7 % ~08m 49s
|++++++ | 10% ~08m 34s
|+++++++ | 14% ~08m 19s
|+++++++++ | 17% ~08m 02s
|+++++++++++ | 21% ~07m 44s
|+++++++++++++ | 24% ~07m 26s
|++++++++++++++ | 28% ~07m 01s
|++++++++++++++++ | 31% ~06m 39s
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|+++++++++++++++++++ | 38% ~05m 58s
|+++++++++++++++++++++ | 41% ~05m 39s
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|+++++++++++++++++++++++++ | 48% ~05m 01s
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|++++++++++++++++++++++++++++ | 55% ~04m 21s
|++++++++++++++++++++++++++++++ | 59% ~04m 01s
|++++++++++++++++++++++++++++++++ | 62% ~03m 40s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 21s
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|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 42s
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|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 42s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 21s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~41s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 52sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~10m 03s
|++++ | 7 % ~08m 57s
|++++++ | 10% ~08m 37s
|+++++++ | 14% ~08m 31s
|+++++++++ | 17% ~08m 17s
|+++++++++++ | 21% ~08m 00s
|+++++++++++++ | 24% ~07m 44s
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|++++++++++++++++++++++++++++++ | 59% ~04m 21s
|++++++++++++++++++++++++++++++++ | 62% ~03m 59s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 38s
|+++++++++++++++++++++++++++++++++++ | 69% ~03m 17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 33s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 12s
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|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 37sExtracting reads overlapping genomic regions
| | 0 % ~calculating
|++ | 3 % ~09m 32s
|++++ | 7 % ~08m 54s
|++++++ | 10% ~08m 52s
|+++++++ | 14% ~09m 04s
|+++++++++ | 17% ~08m 52s
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|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 34s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02m 12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 50s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10m 41s# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)
# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_macs_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_macs_animal = NormalizeData(object = integ_macs_animal, assay = 'GeneAct',
normalization.method = 'LogNormalize',
scale.factor = median(integ_macs_animal$nCount_GeneAct))Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Save object with annotations and gene activities
DefaultAssay(integ_animal) = 'GeneAct'
FeaturePlot(object = integ_animal, features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)
allmk_geneact = FindAllMarkers(object = integ_animal, min.pct = 0.2,
test.use = 'LR', latent.vars = 'nCount_GeneAct')Calculating cluster 0
| | 0 % ~calculating glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+ | 2 % ~10s
|++ | 3 % ~09s
|+++ | 5 % ~10s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~39s
|++ | 2 % ~37s
|++ | 3 % ~37s
|+++ | 5 % ~36s
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|++++++++++++++++ | 30% ~27s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=53s Calculating cluster 3
| | 0 % ~calculating
|+ | 2 % ~17s
|++ | 4 % ~18s
|+++ | 6 % ~17s
|++++ | 8 % ~16s
|+++++ | 9 % ~15s
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|+++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|++++++++++++++++++++++++++++++ | 58% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s Calculating cluster 4
| | 0 % ~calculating
|+ | 2 % ~30s
|++ | 3 % ~28s
|+++ | 5 % ~27s
|++++ | 7 % ~26s
|+++++ | 8 % ~26s
|+++++ | 10% ~25s
|++++++ | 11% ~24s
|+++++++ | 13% ~24s
|++++++++ | 15% ~24s
|+++++++++ | 16% ~41s
|++++++++++ | 18% ~38s
|++++++++++ | 20% ~35s
|+++++++++++ | 21% ~33s
|++++++++++++ | 23% ~31s
|+++++++++++++ | 25% ~30s
|++++++++++++++ | 26% ~28s
|++++++++++++++ | 28% ~27s
|+++++++++++++++ | 30% ~26s
|++++++++++++++++ | 31% ~25s
|+++++++++++++++++ | 33% ~23s
|++++++++++++++++++ | 34% ~22s
|+++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 48% ~16s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|+++++++++++++++++++++++++++ | 52% ~14s
|++++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 59% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|++++++++++++++++++++++++++++++++ | 62% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~01m 11s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 06s
|+++ | 4 % ~01m 05s
|+++ | 5 % ~01m 04s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 03s
|+++++ | 8 % ~01m 02s
|+++++ | 9 % ~01m 01s
|++++++ | 11% ~01m 00s
|++++++ | 12% ~59s
|+++++++ | 13% ~59s
|+++++++ | 14% ~58s
|++++++++ | 15% ~58s
|++++++++ | 16% ~57s
|+++++++++ | 17% ~57s
|+++++++++ | 18% ~56s
|++++++++++ | 19% ~55s
|++++++++++ | 20% ~54s
|+++++++++++ | 21% ~54s
|++++++++++++ | 22% ~53s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~51s
|++++++++++++++ | 26% ~50s
|++++++++++++++ | 27% ~50s
|+++++++++++++++ | 28% ~49s
|+++++++++++++++ | 29% ~48s
|++++++++++++++++ | 31% ~47s
|++++++++++++++++ | 32% ~47s
|+++++++++++++++++ | 33% ~46s
|+++++++++++++++++ | 34% ~45s
|++++++++++++++++++ | 35% ~44s
|++++++++++++++++++ | 36% ~44s
|+++++++++++++++++++ | 37% ~43s
|+++++++++++++++++++ | 38% ~42s
|++++++++++++++++++++ | 39% ~42s
|++++++++++++++++++++ | 40% ~41s
|+++++++++++++++++++++ | 41% ~40s
|++++++++++++++++++++++ | 42% ~39s
|++++++++++++++++++++++ | 43% ~39s
|+++++++++++++++++++++++ | 44% ~38s
|+++++++++++++++++++++++ | 45% ~37s
|++++++++++++++++++++++++ | 46% ~37s
|++++++++++++++++++++++++ | 47% ~36s
|+++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~35s
|++++++++++++++++++++++++++ | 51% ~35s
|++++++++++++++++++++++++++ | 52% ~34s
|+++++++++++++++++++++++++++ | 53% ~34s
|+++++++++++++++++++++++++++ | 54% ~33s
|++++++++++++++++++++++++++++ | 55% ~32s
|++++++++++++++++++++++++++++ | 56% ~32s
|+++++++++++++++++++++++++++++ | 57% ~33s
|+++++++++++++++++++++++++++++ | 58% ~32s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|++++++++++++++++++++++++++++++++ | 62% ~29s
|++++++++++++++++++++++++++++++++ | 63% ~28s
|+++++++++++++++++++++++++++++++++ | 64% ~27s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|++++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|+++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~22s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14sCalculating cluster 6
| | 0 % ~calculating
|+ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 18% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 7
| | 0 % ~calculating
|+ | 2 % ~11s
|++ | 3 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 8 % ~10s
|+++++ | 9 % ~11s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|++++++++ | 14% ~09s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 28% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|++ | 2 % ~01m 11s
|++ | 3 % ~01m 10s
|+++ | 5 % ~01m 09s
|+++ | 6 % ~01m 08s
|++++ | 7 % ~01m 07s
|++++ | 8 % ~01m 06s
|+++++ | 9 % ~01m 06s
|++++++ | 10% ~01m 05s
|++++++ | 11% ~01m 04s
|+++++++ | 12% ~01m 03s
|+++++++ | 14% ~01m 02s
|++++++++ | 15% ~01m 01s
|++++++++ | 16% ~01m 00s
|+++++++++ | 17% ~60s
|++++++++++ | 18% ~59s
|++++++++++ | 19% ~58s
|+++++++++++ | 20% ~57s
|+++++++++++ | 22% ~56s
|++++++++++++ | 23% ~55s
|++++++++++++ | 24% ~54s
|+++++++++++++ | 25% ~53s
|++++++++++++++ | 26% ~52s
|++++++++++++++ | 27% ~52s
|+++++++++++++++ | 28% ~51s
|+++++++++++++++ | 30% ~50s
|++++++++++++++++ | 31% ~49s
|++++++++++++++++ | 32% ~48s
|+++++++++++++++++ | 33% ~48s
|++++++++++++++++++ | 34% ~47s
|++++++++++++++++++ | 35% ~46s
|+++++++++++++++++++ | 36% ~45s
|+++++++++++++++++++ | 38% ~44s
|++++++++++++++++++++ | 39% ~43s
|++++++++++++++++++++ | 40% ~43s
|+++++++++++++++++++++ | 41% ~42s
|++++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~40s
|+++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~39s
|++++++++++++++++++++++++ | 47% ~38s
|++++++++++++++++++++++++ | 48% ~37s
|+++++++++++++++++++++++++ | 49% ~36s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~35s
|+++++++++++++++++++++++++++ | 52% ~34s
|+++++++++++++++++++++++++++ | 53% ~33s
|++++++++++++++++++++++++++++ | 55% ~32s
|++++++++++++++++++++++++++++ | 56% ~31s
|+++++++++++++++++++++++++++++ | 57% ~31s
|+++++++++++++++++++++++++++++ | 58% ~30s
|++++++++++++++++++++++++++++++ | 59% ~29s
|+++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|++++++++++++++++++++++++++++++++ | 62% ~27s
|++++++++++++++++++++++++++++++++ | 64% ~26s
|+++++++++++++++++++++++++++++++++ | 65% ~25s
|+++++++++++++++++++++++++++++++++ | 66% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~24s
|+++++++++++++++++++++++++++++++++++ | 68% ~23s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|++++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 72% ~20s
|+++++++++++++++++++++++++++++++++++++ | 73% ~20s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14sCalculating cluster 9
| | 0 % ~calculating
|++ | 3 % ~06s
|++++ | 6 % ~06s
|+++++ | 9 % ~06s
|+++++++ | 12% ~06s
|++++++++ | 15% ~05s
|++++++++++ | 18% ~05s
|+++++++++++ | 21% ~05s
|+++++++++++++ | 24% ~05s
|++++++++++++++ | 27% ~05s
|++++++++++++++++ | 30% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++++ | 36% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++++ | 42% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++++ | 48% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s Calculating cluster 10
| | 0 % ~calculating
|++ | 2 % ~08s
|+++ | 4 % ~08s
|++++ | 7 % ~08s
|+++++ | 9 % ~08s
|++++++ | 11% ~08s
|+++++++ | 13% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 20% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 24% ~07s
|++++++++++++++ | 26% ~06s
|+++++++++++++++ | 28% ~06s
|++++++++++++++++ | 30% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 50% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster 11
| | 0 % ~calculating
|+ | 2 % ~26s
|++ | 3 % ~24s
|+++ | 5 % ~24s
|++++ | 6 % ~24s
|++++ | 8 % ~24s
|+++++ | 9 % ~23s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 14% ~22s
|++++++++ | 15% ~22s
|+++++++++ | 17% ~21s
|++++++++++ | 18% ~21s
|++++++++++ | 20% ~20s
|+++++++++++ | 22% ~20s
|++++++++++++ | 23% ~19s
|+++++++++++++ | 25% ~19s
|++++++++++++++ | 26% ~19s
|++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~25s
|++++++++++++++++ | 31% ~24s
|+++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 34% ~23s
|++++++++++++++++++ | 35% ~22s
|+++++++++++++++++++ | 37% ~21s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~16s
|++++++++++++++++++++++++++ | 51% ~15s
|+++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~14s
|+++++++++++++++++++++++++++++ | 57% ~13s
|++++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|++++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=28s Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 7 % ~14s
|+++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 20% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|+++++++++++++++ | 28% ~11s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|+++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 41% ~09s
|+++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~01m 21s
|++ | 2 % ~01m 26s
|++ | 4 % ~01m 28s
|+++ | 5 % ~01m 27s
|++++ | 6 % ~01m 25s
|++++ | 7 % ~01m 24s
|+++++ | 9 % ~01m 21s
|+++++ | 10% ~01m 20s
|++++++ | 11% ~01m 19s
|+++++++ | 12% ~01m 18s
|+++++++ | 14% ~01m 17s
|++++++++ | 15% ~01m 16s
|+++++++++ | 16% ~01m 31s
|+++++++++ | 17% ~01m 28s
|++++++++++ | 19% ~01m 26s
|++++++++++ | 20% ~01m 24s
|+++++++++++ | 21% ~01m 22s
|++++++++++++ | 22% ~01m 20s
|++++++++++++ | 23% ~01m 18s
|+++++++++++++ | 25% ~01m 16s
|+++++++++++++ | 26% ~01m 15s
|++++++++++++++ | 27% ~01m 13s
|+++++++++++++++ | 28% ~01m 12s
|+++++++++++++++ | 30% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|+++++++++++++++++ | 32% ~01m 07s
|+++++++++++++++++ | 33% ~01m 06s
|++++++++++++++++++ | 35% ~01m 04s
|++++++++++++++++++ | 36% ~01m 03s
|+++++++++++++++++++ | 37% ~01m 01s
|++++++++++++++++++++ | 38% ~60s
|++++++++++++++++++++ | 40% ~58s
|+++++++++++++++++++++ | 41% ~57s
|+++++++++++++++++++++ | 42% ~56s
|++++++++++++++++++++++ | 43% ~54s
|+++++++++++++++++++++++ | 44% ~53s
|+++++++++++++++++++++++ | 46% ~52s
|++++++++++++++++++++++++ | 47% ~50s
|+++++++++++++++++++++++++ | 48% ~49s
|+++++++++++++++++++++++++ | 49% ~48s
|++++++++++++++++++++++++++ | 51% ~47s
|++++++++++++++++++++++++++ | 52% ~45s
|+++++++++++++++++++++++++++ | 53% ~44s
|++++++++++++++++++++++++++++ | 54% ~43s
|++++++++++++++++++++++++++++ | 56% ~41s
|+++++++++++++++++++++++++++++ | 57% ~40s
|++++++++++++++++++++++++++++++ | 58% ~39s
|++++++++++++++++++++++++++++++ | 59% ~38s
|+++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 62% ~35s
|++++++++++++++++++++++++++++++++ | 63% ~34s
|+++++++++++++++++++++++++++++++++ | 64% ~33s
|+++++++++++++++++++++++++++++++++ | 65% ~32s
|++++++++++++++++++++++++++++++++++ | 67% ~31s
|++++++++++++++++++++++++++++++++++ | 68% ~30s
|+++++++++++++++++++++++++++++++++++ | 69% ~28s
|++++++++++++++++++++++++++++++++++++ | 70% ~27s
|++++++++++++++++++++++++++++++++++++ | 72% ~26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~25s
|++++++++++++++++++++++++++++++++++++++ | 74% ~24s
|++++++++++++++++++++++++++++++++++++++ | 75% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 34sCalculating cluster 14
| | 0 % ~calculating
|+ | 1 % ~06m 51s
|+ | 2 % ~06m 40s
|++ | 3 % ~06m 37s
|++ | 4 % ~06m 34s
|+++ | 5 % ~06m 31s
|+++ | 6 % ~06m 27s
|++++ | 7 % ~06m 24s
|++++ | 8 % ~06m 21s
|+++++ | 9 % ~06m 17s
|+++++ | 10% ~06m 14s
|++++++ | 11% ~06m 13s
|++++++ | 12% ~06m 30s
|+++++++ | 13% ~06m 24s
|+++++++ | 14% ~06m 19s
|++++++++ | 15% ~06m 13s
|++++++++ | 16% ~06m 07s
|+++++++++ | 17% ~06m 02s
|+++++++++ | 18% ~05m 56s
|++++++++++ | 19% ~05m 51s
|++++++++++ | 20% ~05m 46s
|+++++++++++ | 21% ~05m 41s
|+++++++++++ | 22% ~05m 36s
|++++++++++++ | 23% ~05m 32s
|++++++++++++ | 24% ~05m 27s
|+++++++++++++ | 25% ~05m 23s
|+++++++++++++ | 26% ~05m 18s
|++++++++++++++ | 27% ~05m 15s
|++++++++++++++ | 28% ~05m 11s
|+++++++++++++++ | 29% ~05m 13s
|+++++++++++++++ | 30% ~05m 09s
|++++++++++++++++ | 31% ~05m 04s
|++++++++++++++++ | 32% ~04m 59s
|+++++++++++++++++ | 33% ~04m 54s
|+++++++++++++++++ | 34% ~04m 49s
|++++++++++++++++++ | 35% ~04m 44s
|++++++++++++++++++ | 36% ~04m 40s
|+++++++++++++++++++ | 37% ~04m 35s
|+++++++++++++++++++ | 38% ~04m 30s
|++++++++++++++++++++ | 39% ~04m 25s
|++++++++++++++++++++ | 40% ~04m 21s
|+++++++++++++++++++++ | 41% ~04m 17s
|+++++++++++++++++++++ | 42% ~04m 12s
|++++++++++++++++++++++ | 43% ~04m 08s
|++++++++++++++++++++++ | 44% ~04m 04s
|+++++++++++++++++++++++ | 45% ~03m 60s
|+++++++++++++++++++++++ | 46% ~03m 59s
|++++++++++++++++++++++++ | 47% ~03m 54s
|++++++++++++++++++++++++ | 48% ~03m 50s
|+++++++++++++++++++++++++ | 49% ~03m 45s
|+++++++++++++++++++++++++ | 50% ~03m 40s
|++++++++++++++++++++++++++ | 51% ~03m 36s
|++++++++++++++++++++++++++ | 52% ~03m 31s
|+++++++++++++++++++++++++++ | 53% ~03m 26s
|+++++++++++++++++++++++++++ | 54% ~03m 22s
|++++++++++++++++++++++++++++ | 55% ~03m 17s
|++++++++++++++++++++++++++++ | 56% ~03m 13s
|+++++++++++++++++++++++++++++ | 57% ~03m 08s
|+++++++++++++++++++++++++++++ | 58% ~03m 04s
|++++++++++++++++++++++++++++++ | 59% ~02m 59s
|++++++++++++++++++++++++++++++ | 60% ~02m 55s
|+++++++++++++++++++++++++++++++ | 61% ~02m 51s
|+++++++++++++++++++++++++++++++ | 62% ~02m 47s
|++++++++++++++++++++++++++++++++ | 63% ~02m 44s
|++++++++++++++++++++++++++++++++ | 64% ~02m 39s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 35s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 30s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 26s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 21s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 17s
|+++++++++++++++++++++++++++++++++++ | 70% ~02m 12s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 08s
|++++++++++++++++++++++++++++++++++++ | 72% ~02m 03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 59s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 54s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 50s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 46s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 41s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 32s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 24s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~57s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~53s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~48s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~44s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~40s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~35s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~31s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07m 20sCalculating cluster 15
Calculating cluster 16
The following tests were not performed: When testing 15 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 16 versus all:
Cell group 1 has fewer than 3 cellsallmk_geneact = allmk_geneact[allmk_geneact$p_val_adj<=0.05,]
write.csv(allmk_geneact, file = "results/multiome/allmk_geneact.csv",
col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredDefaultAssay(integ_macs_animal) = 'GeneAct'
FeaturePlot(object = integ_macs_animal,
features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)
allmk_geneact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2,
test.use = 'LR', latent.vars = 'nCount_GeneAct')Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~12s
|++ | 4 % ~12s
|+++ | 5 % ~12s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|++++++ | 11% ~11s
|++++++ | 12% ~11s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~10s
|++++++++++ | 20% ~10s
|+++++++++++ | 21% ~09s
|++++++++++++ | 22% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 1
| | 0 % ~calculating glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++ | 33% ~01s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++ | 67% ~00s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~49s
|++ | 2 % ~47s
|++ | 3 % ~46s
|+++ | 4 % ~02m 19s
|+++ | 5 % ~01m 59s
|++++ | 7 % ~01m 46s
|++++ | 8 % ~01m 36s
|+++++ | 9 % ~01m 29s
|+++++ | 10% ~01m 23s
|++++++ | 11% ~01m 18s
|+++++++ | 12% ~01m 14s
|+++++++ | 13% ~01m 11s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 05s
|+++++++++ | 16% ~01m 03s
|+++++++++ | 18% ~01m 01s
|++++++++++ | 19% ~59s
|++++++++++ | 20% ~57s
|+++++++++++ | 21% ~55s
|+++++++++++ | 22% ~54s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~49s
|++++++++++++++ | 26% ~48s
|++++++++++++++ | 27% ~47s
|+++++++++++++++ | 29% ~46s
|+++++++++++++++ | 30% ~45s
|++++++++++++++++ | 31% ~44s
|++++++++++++++++ | 32% ~43s
|+++++++++++++++++ | 33% ~42s
|++++++++++++++++++ | 34% ~41s
|++++++++++++++++++ | 35% ~40s
|+++++++++++++++++++ | 36% ~39s
|+++++++++++++++++++ | 37% ~38s
|++++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 40% ~36s
|+++++++++++++++++++++ | 41% ~35s
|+++++++++++++++++++++ | 42% ~35s
|++++++++++++++++++++++ | 43% ~34s
|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
|++++++++++++++++++++++++ | 46% ~31s
|++++++++++++++++++++++++ | 47% ~31s
|+++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~29s
|++++++++++++++++++++++++++ | 51% ~28s
|++++++++++++++++++++++++++ | 52% ~28s
|+++++++++++++++++++++++++++ | 53% ~27s
|+++++++++++++++++++++++++++ | 54% ~26s
|++++++++++++++++++++++++++++ | 55% ~25s
|+++++++++++++++++++++++++++++ | 56% ~25s
|+++++++++++++++++++++++++++++ | 57% ~24s
|++++++++++++++++++++++++++++++ | 58% ~23s
|++++++++++++++++++++++++++++++ | 59% ~23s
|+++++++++++++++++++++++++++++++ | 60% ~22s
|+++++++++++++++++++++++++++++++ | 62% ~21s
|++++++++++++++++++++++++++++++++ | 63% ~21s
|++++++++++++++++++++++++++++++++ | 64% ~20s
|+++++++++++++++++++++++++++++++++ | 65% ~19s
|+++++++++++++++++++++++++++++++++ | 66% ~19s
|++++++++++++++++++++++++++++++++++ | 67% ~18s
|+++++++++++++++++++++++++++++++++++ | 68% ~17s
|+++++++++++++++++++++++++++++++++++ | 69% ~17s
|++++++++++++++++++++++++++++++++++++ | 70% ~16s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|+++++++++++++++++++++++++++++++++++++ | 73% ~15s
|+++++++++++++++++++++++++++++++++++++ | 74% ~14s
|++++++++++++++++++++++++++++++++++++++ | 75% ~14s
|++++++++++++++++++++++++++++++++++++++ | 76% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=53s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~18s
|++ | 2 % ~20s
|++ | 3 % ~19s
|+++ | 4 % ~18s
|+++ | 5 % ~17s
|++++ | 6 % ~17s
|++++ | 7 % ~17s
|+++++ | 8 % ~17s
|+++++ | 9 % ~17s
|++++++ | 10% ~17s
|++++++ | 11% ~17s
|+++++++ | 12% ~17s
|+++++++ | 13% ~16s
|++++++++ | 14% ~16s
|++++++++ | 15% ~16s
|+++++++++ | 16% ~16s
|+++++++++ | 17% ~15s
|++++++++++ | 18% ~15s
|++++++++++ | 19% ~15s
|+++++++++++ | 20% ~15s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~14s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~12s
|++++++++++++++++++ | 36% ~11s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~09s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|++++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|++++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~09s
|+++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~54s
|++ | 2 % ~52s
|++ | 3 % ~51s
|+++ | 5 % ~50s
|+++ | 6 % ~49s
|++++ | 7 % ~49s
|+++++ | 8 % ~48s
|+++++ | 9 % ~47s
|++++++ | 10% ~47s
|++++++ | 11% ~46s
|+++++++ | 13% ~45s
|+++++++ | 14% ~45s
|++++++++ | 15% ~44s
|+++++++++ | 16% ~43s
|+++++++++ | 17% ~43s
|++++++++++ | 18% ~42s
|++++++++++ | 20% ~42s
|+++++++++++ | 21% ~41s
|+++++++++++ | 22% ~40s
|++++++++++++ | 23% ~39s
|+++++++++++++ | 24% ~39s
|+++++++++++++ | 25% ~38s
|++++++++++++++ | 26% ~38s
|++++++++++++++ | 28% ~37s
|+++++++++++++++ | 29% ~36s
|+++++++++++++++ | 30% ~36s
|++++++++++++++++ | 31% ~35s
|+++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|++++++++++++++++++ | 34% ~33s
|++++++++++++++++++ | 36% ~33s
|+++++++++++++++++++ | 37% ~32s
|+++++++++++++++++++ | 38% ~32s
|++++++++++++++++++++ | 39% ~31s
|+++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~30s
|++++++++++++++++++++++ | 43% ~29s
|++++++++++++++++++++++ | 44% ~29s
|+++++++++++++++++++++++ | 45% ~28s
|+++++++++++++++++++++++ | 46% ~27s
|++++++++++++++++++++++++ | 47% ~27s
|+++++++++++++++++++++++++ | 48% ~26s
|+++++++++++++++++++++++++ | 49% ~26s
|++++++++++++++++++++++++++ | 51% ~25s
|++++++++++++++++++++++++++ | 52% ~24s
|+++++++++++++++++++++++++++ | 53% ~24s
|++++++++++++++++++++++++++++ | 54% ~23s
|++++++++++++++++++++++++++++ | 55% ~23s
|+++++++++++++++++++++++++++++ | 56% ~22s
|+++++++++++++++++++++++++++++ | 57% ~22s
|++++++++++++++++++++++++++++++ | 59% ~21s
|++++++++++++++++++++++++++++++ | 60% ~21s
|+++++++++++++++++++++++++++++++ | 61% ~20s
|++++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~19s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 66% ~18s
|++++++++++++++++++++++++++++++++++ | 67% ~17s
|++++++++++++++++++++++++++++++++++ | 68% ~17s
|+++++++++++++++++++++++++++++++++++ | 69% ~16s
|++++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|+++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 74% ~14s
|++++++++++++++++++++++++++++++++++++++ | 75% ~13s
|++++++++++++++++++++++++++++++++++++++ | 76% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=55s Calculating cluster 5
| | 0 % ~calculating
|++ | 2 % ~09s
|+++ | 4 % ~09s
|++++ | 6 % ~08s
|+++++ | 8 % ~08s
|++++++ | 10% ~08s
|+++++++ | 12% ~08s
|++++++++ | 15% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 21% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|++++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 56% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02m 25s
|++ | 2 % ~02m 20s
|++ | 3 % ~02m 16s
|+++ | 4 % ~02m 15s
|+++ | 6 % ~02m 12s
|++++ | 7 % ~02m 11s
|++++ | 8 % ~02m 10s
|+++++ | 9 % ~02m 08s
|+++++ | 10% ~02m 07s
|++++++ | 11% ~02m 06s
|+++++++ | 12% ~02m 04s
|+++++++ | 13% ~02m 02s
|++++++++ | 14% ~02m 00s
|++++++++ | 16% ~01m 59s
|+++++++++ | 17% ~01m 58s
|+++++++++ | 18% ~01m 57s
|++++++++++ | 19% ~01m 55s
|++++++++++ | 20% ~01m 54s
|+++++++++++ | 21% ~01m 53s
|++++++++++++ | 22% ~01m 52s
|++++++++++++ | 23% ~01m 51s
|+++++++++++++ | 24% ~01m 60s
|+++++++++++++ | 26% ~01m 58s
|++++++++++++++ | 27% ~01m 56s
|++++++++++++++ | 28% ~01m 54s
|+++++++++++++++ | 29% ~01m 52s
|+++++++++++++++ | 30% ~01m 50s
|++++++++++++++++ | 31% ~01m 48s
|+++++++++++++++++ | 32% ~01m 46s
|+++++++++++++++++ | 33% ~01m 44s
|++++++++++++++++++ | 34% ~01m 42s
|++++++++++++++++++ | 36% ~01m 40s
|+++++++++++++++++++ | 37% ~01m 38s
|+++++++++++++++++++ | 38% ~01m 36s
|++++++++++++++++++++ | 39% ~01m 34s
|++++++++++++++++++++ | 40% ~01m 32s
|+++++++++++++++++++++ | 41% ~01m 30s
|++++++++++++++++++++++ | 42% ~01m 28s
|++++++++++++++++++++++ | 43% ~01m 26s
|+++++++++++++++++++++++ | 44% ~01m 24s
|+++++++++++++++++++++++ | 46% ~01m 22s
|++++++++++++++++++++++++ | 47% ~01m 20s
|++++++++++++++++++++++++ | 48% ~01m 19s
|+++++++++++++++++++++++++ | 49% ~01m 17s
|+++++++++++++++++++++++++ | 50% ~01m 15s
|++++++++++++++++++++++++++ | 51% ~01m 13s
|+++++++++++++++++++++++++++ | 52% ~01m 12s
|+++++++++++++++++++++++++++ | 53% ~01m 10s
|++++++++++++++++++++++++++++ | 54% ~01m 08s
|++++++++++++++++++++++++++++ | 56% ~01m 07s
|+++++++++++++++++++++++++++++ | 57% ~01m 05s
|+++++++++++++++++++++++++++++ | 58% ~01m 03s
|++++++++++++++++++++++++++++++ | 59% ~01m 02s
|++++++++++++++++++++++++++++++ | 60% ~01m 00s
|+++++++++++++++++++++++++++++++ | 61% ~58s
|++++++++++++++++++++++++++++++++ | 62% ~57s
|++++++++++++++++++++++++++++++++ | 63% ~55s
|+++++++++++++++++++++++++++++++++ | 64% ~53s
|+++++++++++++++++++++++++++++++++ | 66% ~52s
|++++++++++++++++++++++++++++++++++ | 67% ~50s
|++++++++++++++++++++++++++++++++++ | 68% ~48s
|+++++++++++++++++++++++++++++++++++ | 69% ~47s
|+++++++++++++++++++++++++++++++++++ | 70% ~45s
|++++++++++++++++++++++++++++++++++++ | 71% ~45s
|+++++++++++++++++++++++++++++++++++++ | 72% ~43s
|+++++++++++++++++++++++++++++++++++++ | 73% ~41s
|++++++++++++++++++++++++++++++++++++++ | 74% ~39s
|++++++++++++++++++++++++++++++++++++++ | 76% ~38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~36s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~34s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~31s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~26s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 30sCalculating cluster 7
| | 0 % ~calculating glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++ | 14% ~02s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++ | 29% ~01s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++ | 43% ~01s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++ | 57% ~01s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++ | 71% ~01s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~13s
|++++ | 7 % ~14s
|+++++ | 8 % ~14s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|+++++++++ | 16% ~12s
|+++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|+++++++++++++++ | 29% ~11s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|+++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|+++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|+++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Calculating cluster 9
| | 0 % ~calculating
|++ | 2 % ~08s
|+++ | 5 % ~08s
|++++ | 7 % ~07s
|+++++ | 9 % ~07s
|++++++ | 12% ~07s
|+++++++ | 14% ~07s
|+++++++++ | 16% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 21% ~07s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 28% ~06s
|++++++++++++++++ | 30% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 42% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|++++++++++++++++++++++++++++++ | 58% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 10
| | 0 % ~calculating
|++ | 3 % ~07s
|+++ | 5 % ~07s
|++++ | 8 % ~07s
|++++++ | 10% ~07s
|+++++++ | 13% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 18% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 26% ~06s
|+++++++++++++++ | 28% ~06s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 36% ~05s
|++++++++++++++++++++ | 38% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 44% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|+++++++++++++++++++++++++++++ | 56% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~16s
|++ | 3 % ~15s
|++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 8 % ~15s
|+++++ | 9 % ~15s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 13% ~14s
|+++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~13s
|+++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~13s
|+++++++++++ | 22% ~13s
|++++++++++++ | 23% ~13s
|+++++++++++++ | 24% ~12s
|+++++++++++++ | 25% ~12s
|++++++++++++++ | 27% ~12s
|++++++++++++++ | 28% ~12s
|+++++++++++++++ | 29% ~12s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~11s
|++++++++++++++++++ | 35% ~11s
|+++++++++++++++++++ | 37% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|+++++++++++++++++++++ | 41% ~10s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~09s
|+++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~09s
|+++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|++++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~40s
|++ | 2 % ~40s
|++ | 3 % ~39s
|+++ | 4 % ~38s
|+++ | 6 % ~37s
|++++ | 7 % ~37s
|++++ | 8 % ~36s
|+++++ | 9 % ~36s
|+++++ | 10% ~36s
|++++++ | 11% ~35s
|+++++++ | 12% ~35s
|+++++++ | 13% ~34s
|++++++++ | 14% ~34s
|++++++++ | 16% ~33s
|+++++++++ | 17% ~33s
|+++++++++ | 18% ~32s
|++++++++++ | 19% ~32s
|++++++++++ | 20% ~31s
|+++++++++++ | 21% ~31s
|++++++++++++ | 22% ~30s
|++++++++++++ | 23% ~30s
|+++++++++++++ | 24% ~30s
|+++++++++++++ | 26% ~29s
|++++++++++++++ | 27% ~29s
|++++++++++++++ | 28% ~28s
|+++++++++++++++ | 29% ~28s
|+++++++++++++++ | 30% ~27s
|++++++++++++++++ | 31% ~27s
|+++++++++++++++++ | 32% ~26s
|+++++++++++++++++ | 33% ~26s
|++++++++++++++++++ | 34% ~25s
|++++++++++++++++++ | 36% ~27s
|+++++++++++++++++++ | 37% ~26s
|+++++++++++++++++++ | 38% ~26s
|++++++++++++++++++++ | 39% ~25s
|++++++++++++++++++++ | 40% ~25s
|+++++++++++++++++++++ | 41% ~24s
|++++++++++++++++++++++ | 42% ~24s
|++++++++++++++++++++++ | 43% ~23s
|+++++++++++++++++++++++ | 44% ~23s
|+++++++++++++++++++++++ | 46% ~22s
|++++++++++++++++++++++++ | 47% ~22s
|++++++++++++++++++++++++ | 48% ~21s
|+++++++++++++++++++++++++ | 49% ~21s
|+++++++++++++++++++++++++ | 50% ~20s
|++++++++++++++++++++++++++ | 51% ~20s
|+++++++++++++++++++++++++++ | 52% ~19s
|+++++++++++++++++++++++++++ | 53% ~19s
|++++++++++++++++++++++++++++ | 54% ~18s
|++++++++++++++++++++++++++++ | 56% ~18s
|+++++++++++++++++++++++++++++ | 57% ~17s
|+++++++++++++++++++++++++++++ | 58% ~17s
|++++++++++++++++++++++++++++++ | 59% ~16s
|++++++++++++++++++++++++++++++ | 60% ~16s
|+++++++++++++++++++++++++++++++ | 61% ~15s
|++++++++++++++++++++++++++++++++ | 62% ~15s
|++++++++++++++++++++++++++++++++ | 63% ~14s
|+++++++++++++++++++++++++++++++++ | 64% ~14s
|+++++++++++++++++++++++++++++++++ | 66% ~14s
|++++++++++++++++++++++++++++++++++ | 67% ~13s
|++++++++++++++++++++++++++++++++++ | 68% ~13s
|+++++++++++++++++++++++++++++++++++ | 69% ~12s
|+++++++++++++++++++++++++++++++++++ | 70% ~12s
|++++++++++++++++++++++++++++++++++++ | 71% ~11s
|+++++++++++++++++++++++++++++++++++++ | 72% ~11s
|+++++++++++++++++++++++++++++++++++++ | 73% ~10s
|++++++++++++++++++++++++++++++++++++++ | 74% ~10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=39s Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~14m 32s
|++ | 2 % ~11m 36s
|++ | 3 % ~10m 32s
|+++ | 4 % ~09m 57s
|+++ | 5 % ~09m 35s
|++++ | 6 % ~09m 16s
|++++ | 7 % ~09m 02s
|+++++ | 8 % ~08m 49s
|+++++ | 9 % ~08m 38s
|++++++ | 10% ~08m 29s
|++++++ | 11% ~08m 23s
|+++++++ | 12% ~08m 17s
|+++++++ | 13% ~08m 11s
|++++++++ | 14% ~08m 03s
|++++++++ | 15% ~08m 13s
|+++++++++ | 16% ~08m 05s
|+++++++++ | 18% ~07m 57s
|++++++++++ | 19% ~07m 50s
|++++++++++ | 20% ~07m 42s
|+++++++++++ | 21% ~07m 34s
|+++++++++++ | 22% ~07m 26s
|++++++++++++ | 23% ~07m 19s
|++++++++++++ | 24% ~07m 12s
|+++++++++++++ | 25% ~07m 05s
|+++++++++++++ | 26% ~06m 60s
|++++++++++++++ | 27% ~06m 54s
|++++++++++++++ | 28% ~06m 48s
|+++++++++++++++ | 29% ~06m 41s
|+++++++++++++++ | 30% ~06m 42s
|++++++++++++++++ | 31% ~06m 35s
|++++++++++++++++ | 32% ~06m 29s
|+++++++++++++++++ | 33% ~06m 22s
|++++++++++++++++++ | 34% ~06m 15s
|++++++++++++++++++ | 35% ~06m 09s
|+++++++++++++++++++ | 36% ~06m 02s
|+++++++++++++++++++ | 37% ~05m 56s
|++++++++++++++++++++ | 38% ~05m 50s
|++++++++++++++++++++ | 39% ~05m 44s
|+++++++++++++++++++++ | 40% ~05m 38s
|+++++++++++++++++++++ | 41% ~05m 32s
|++++++++++++++++++++++ | 42% ~05m 26s
|++++++++++++++++++++++ | 43% ~05m 24s
|+++++++++++++++++++++++ | 44% ~05m 18s
|+++++++++++++++++++++++ | 45% ~05m 11s
|++++++++++++++++++++++++ | 46% ~05m 05s
|++++++++++++++++++++++++ | 47% ~04m 59s
|+++++++++++++++++++++++++ | 48% ~04m 52s
|+++++++++++++++++++++++++ | 49% ~04m 46s
|++++++++++++++++++++++++++ | 51% ~04m 40s
|++++++++++++++++++++++++++ | 52% ~04m 34s
|+++++++++++++++++++++++++++ | 53% ~04m 28s
|+++++++++++++++++++++++++++ | 54% ~04m 22s
|++++++++++++++++++++++++++++ | 55% ~04m 16s
|++++++++++++++++++++++++++++ | 56% ~04m 10s
|+++++++++++++++++++++++++++++ | 57% ~04m 04s
|+++++++++++++++++++++++++++++ | 58% ~04m 00s
|++++++++++++++++++++++++++++++ | 59% ~03m 54s
|++++++++++++++++++++++++++++++ | 60% ~03m 48s
|+++++++++++++++++++++++++++++++ | 61% ~03m 42s
|+++++++++++++++++++++++++++++++ | 62% ~03m 36s
|++++++++++++++++++++++++++++++++ | 63% ~03m 30s
|++++++++++++++++++++++++++++++++ | 64% ~03m 24s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 18s
|+++++++++++++++++++++++++++++++++ | 66% ~03m 12s
|++++++++++++++++++++++++++++++++++ | 67% ~03m 06s
|+++++++++++++++++++++++++++++++++++ | 68% ~03m 00s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 54s
|++++++++++++++++++++++++++++++++++++ | 70% ~02m 48s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 43s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02m 38s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02m 32s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02m 26s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 20s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02m 14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02m 08s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02m 02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 56s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 50s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 44s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 39s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 33s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 27s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01m 04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~58s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~52s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~46s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~41s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~35s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09m 24sCalculating cluster 14
Calculating cluster 15
The following tests were not performed: When testing 14 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 15 versus all:
Cell group 1 has fewer than 3 cellsallmk_geneact_macs = allmk_geneact_macs[allmk_geneact_macs$p_val_adj<=0.05,]
write.csv(allmk_geneact_macs, file = "results/multiome/allmk_geneact_macs.csv",
col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredMotif analysis
Add motif information to the ATAC object
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters.RDS")Determine enriched Motifs from group of regions
# Get a list of motif position frequency matrices from the JASPAR database - vertebrates
pfm = getMatrixSet(JASPAR2020, opts = list(tax_group = "vertebrates", all_versions = F))
DefaultAssay(integ_animal) = "ATAC"
# add motif information
integ_animal = AddMotifs(object = integ_animal, pfm = pfm, assay = "ATAC",
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)Building motif matrix
Finding motif positions
Creating Motif objectDefaultAssay(integ_macs_animal) = "ATAC"
# add motif information
integ_macs_animal = AddMotifs(object = integ_macs_animal, pfm = pfm, assay = "ATAC",
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)Building motif matrix
Finding motif positions
Creating Motif objectPlot top enriched motifs
MotifPlot(integ_animal, motifs = head(rownames(enr_motifs_atac)))
MotifPlot(integ_macs_animal, motifs = head(rownames(enr_motifs_macs)))Compute motif activity
MotifPlot(integ_animal, motifs = head(rownames(enr_motifs_atac)))
MotifPlot(integ_macs_animal, motifs = head(rownames(enr_motifs_macs)))
DE motifs by activity
integ_animal = RunChromVAR(integ_animal,
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)Computing GC bias per region
Selecting background regions
Computing deviations from background
Constructing chromVAR assayDefaultAssay(integ_animal) = 'chromvar'
# look at the activity of Mef2c
FeaturePlot(object = integ_animal, features = "MA1099.2", min.cutoff = 'q10',
max.cutoff = 'q90', pt.size = 0.1)
integ_macs_animal = RunChromVAR(integ_macs_animal,
genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)Computing GC bias per region
Selecting background regions
Computing deviations from background
Constructing chromVAR assayDefaultAssay(integ_macs_animal) = 'chromvar'
# look at the activity of Mef2c
FeaturePlot(object = integ_macs_animal, features = "MA1099.2", min.cutoff = 'q10',
max.cutoff = 'q90', pt.size = 0.1)
TF footprinting (needs access to fragments) - VERY SLOW, do for relevant ones only
DefaultAssay(integ_animal) = 'chromvar'
diff_motifact = FindAllMarkers(object = integ_animal, min.pct = 0.2, test.use = 'LR',
latent.vars = 'nCount_ATAC', verbose = F)glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 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1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredThe following tests were not performed: When testing 15 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 16 versus all:
Cell group 1 has fewer than 3 cellsdiff_motifact = diff_motifact[diff_motifact$p_val_adj<=0.05,]
diff_motifact$name = unlist(integ_animal@assays$ATAC@motifs@motif.names[diff_motifact$gene])
write.csv(diff_motifact, file = "results/multiome/diff_motifact_atac.csv",
col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredMotifPlot(integ_animal, motifs = head(diff_motifact$gene), assay = 'ATAC')
DefaultAssay(integ_macs_animal) = 'chromvar'
diff_motifact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, test.use = 'LR',
latent.vars = 'nCount_ATAC', verbose = F)glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 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1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 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probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredThe following tests were not performed: When testing 14 versus all:
Cell group 1 has fewer than 3 cellsWhen testing 15 versus all:
Cell group 1 has fewer than 3 cellsdiff_motifact_macs = diff_motifact_macs[diff_motifact_macs$p_val_adj<=0.05,]
diff_motifact_macs$name = unlist(integ_macs_animal@assays$ATAC@motifs@motif.names[diff_motifact_macs$gene])
write.csv(diff_motifact_macs, file = "results/multiome/diff_motifact_macs.csv",
col.names = T, row.names = F, quote = F)attempt to set 'col.names' ignoredMotifPlot(integ_macs_animal, motifs = head(diff_motifact_macs$gene), assay = 'ATAC')
Save object with annotations and gene activities
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters_motifs.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")Adding snRNA-seq
Load data
Load ATAC data again
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters_motifs.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")Load all snRNA-seq (multiome and other)
rna_dat = readRDS("data/expression/axolotl_reclust/all_nuclei_clustered_highlevel_anno.RDS")Filter non-multiome data
rna_dat = rna_dat[,rna_dat$chem=="multiome"]Reformat cell names to match those in ATAC
rna_dat = rna_dat[,rna_dat$chem=="multiome"]Overlap in cells with ATAC
newcellnames = colnames(rna_dat)
newcellnames = gsub("-1_1", "-a1-1", newcellnames)
newcellnames = gsub("-1_2", "-a1-2", newcellnames)
newcellnames = gsub("-1_3", "-a3-1", newcellnames)
newcellnames = gsub("-1_4", "-a3-2", newcellnames)
rna_dat = RenameCells(rna_dat, new.names = newcellnames)Normalise RNA data
Subset and SCT normalise
par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_animal)))
title("all")for(s in c("a1-1","a1-2","a3-1","a3-2")){
gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))],
"ATAC" = colnames(integ_animal)[grepl(s,colnames(integ_animal))]))
title(s)
}
par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_macs_animal)))
title("all")for(s in c("a1-1","a1-2","a3-1","a3-2")){
gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))],
"ATAC" = colnames(integ_macs_animal)[grepl(s,colnames(integ_macs_animal))]))
title(s)
}
Get PCA and UMAP for RNA
sub_rna_atac = rna_dat[,colnames(rna_dat) %in% colnames(integ_animal)]
sub_rna_atac = NormalizeData(sub_rna_atac)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|sub_rna_atac = FindVariableFeatures(sub_rna_atac)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|sub_rna_atac = ScaleData(sub_rna_atac, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))Regressing out nCount_RNA, nFeature_RNA, percent.mt
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|==========================================================================================================================================================================| 100%sub_rna_macs = rna_dat[,colnames(rna_dat) %in% colnames(integ_macs_animal)]
sub_rna_macs = NormalizeData(sub_rna_macs)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|sub_rna_macs = FindVariableFeatures(sub_rna_macs)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|sub_rna_macs = ScaleData(sub_rna_macs, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))Regressing out nCount_RNA, nFeature_RNA, percent.mt
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Centering and scaling data matrix
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|==========================================================================================================================================================================| 100%Add RNA to ATAC data
Subset ATAC data
sub_rna_atac = RunPCA(sub_rna_atac, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_atac, ndims = 50)
sub_rna_atac = RunUMAP(sub_rna_atac, reduction = "pca", dims = 1:40, verbose = F)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
Warning: Cannot add objects with duplicate keys (offending key: UMAP_), setting key to 'umap_'sub_rna_macs = RunPCA(sub_rna_macs, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_macs, ndims = 50)
sub_rna_macs = RunUMAP(sub_rna_macs, reduction = "pca", dims = 1:40, verbose = F)Warning: Cannot add objects with duplicate keys (offending key: UMAP_), setting key to 'umap_'Processing ATAC
atac_multiome = integ_animal[,colnames(integ_animal) %in% colnames(sub_rna_atac)]
macs_multiome = integ_macs_animal[,colnames(integ_macs_animal) %in% colnames(sub_rna_macs)]Re-integrating ATAC-seq
Processing ATAC-seq by animal
Integrate based on each animal - CellRanger peaks
multiome_atac_animal = SplitObject(atac_multiome, split.by = "animal")
for(n in names(multiome_atac_animal)){
multiome_atac_animal[[n]] = RunTFIDF(multiome_atac_animal[[n]])
multiome_atac_animal[[n]] = FindTopFeatures(multiome_atac_animal[[n]], min.cutoff = "q0")
multiome_atac_animal[[n]] = RunSVD(multiome_atac_animal[[n]])
}Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddingsDepthCor(multiome_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_atac_animal$a3, reduction = "lsi", n = 30)
multiome_macs_animal = SplitObject(macs_multiome, split.by = "animal")
for(n in names(multiome_macs_animal)){
multiome_macs_animal[[n]] = RunTFIDF(multiome_macs_animal[[n]])
multiome_macs_animal[[n]] = FindTopFeatures(multiome_macs_animal[[n]], min.cutoff = "q0")
multiome_macs_animal[[n]] = RunSVD(multiome_macs_animal[[n]])
}Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddings
Performing TF-IDF normalization
Warning in RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), :
Some features contain 0 total counts
Running SVD
Scaling cell embeddingsDepthCor(multiome_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_macs_animal$a3, reduction = "lsi", n = 30)
Integrate based on each animal - MACS peaks
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_atac_animal,
reduction = "rlsi", dims = c(2:4,6:30),
anchor.features = rownames(multiome_atac_animal))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Finding all pairwise anchors
| | 0 % ~calculating Warning: No filtering performed if passing to data rather than counts
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 803 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 22s# integrate LSI embeddings
mome_atac = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
reductions = atac_multiome[["lsi"]],
new.reduction.name = "integrated_lsi")Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(mome_atac, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
mome_atac = RunUMAP(mome_atac, reduction = "integrated_lsi", dims = c(2:4,6:30), verbose = F)
DimPlot(mome_atac, group.by = "dataset")
FeaturePlot(object = mome_atac, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Add RNA to ATAC
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_macs_animal,
reduction = "rlsi", dims = c(2:3,6:30),
anchor.features = rownames(multiome_macs_animal))Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s Finding all pairwise anchors
| | 0 % ~calculating Warning: No filtering performed if passing to data rather than counts
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 537 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 37s# integrate LSI embeddings
mome_macs = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
reductions = macs_multiome[["lsi"]],
new.reduction.name = "integrated_lsi")Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataDepthCor(mome_macs, reduction = "integrated_lsi", n = 30)
# create a new UMAP using the integrated embeddings
mome_macs = RunUMAP(mome_macs, reduction = "integrated_lsi", dims = c(2:3,5:30), verbose = F)
DimPlot(mome_macs, group.by = "dataset")
FeaturePlot(object = mome_macs, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
pt.size = 0.1, ncol = 2)
Save multiome data
mome_atac[["RNA"]] = sub_rna_atac@assays$RNA
mome_atac@reductions$pca = sub_rna_atac@reductions$pca
mome_macs[["RNA"]] = sub_rna_macs@assays$RNA
mome_macs@reductions$pca = sub_rna_macs@reductions$pcaPlot ATAC with regions and cellclusters
saveRDS(mome_atac, file = "data/processed/multiome/multiome_integATAC_SCT.RDS")Warning in gzfile(file, mode) :
cannot open compressed file 'data/processed/multiome/multiome_integATAC_SCT.RDS', probable reason 'No such file or directory'
Error in gzfile(file, mode) : cannot open the connectionIntegrated processing
Load multiome data
pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf", height = 3, width = 3.2)
Error in pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf", :
cannot open file 'results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf'Combined projections
Prepare segregated peaks. This is done using the annotation based on the original GTF file
mome_atac_SCT = readRDS(file = "data/processed/multiome/multiome_integATAC_SCT.RDS")
mome_macs_SCT = readRDS(file = "data/processed/multiome/multiome_integMACS_SCT.RDS")Multiome factor analysis - CellRanger peaks
# CellRanger peaks
DefaultAssay(mome_atac_SCT) = "ATAC"
transc = Annotation(mome_atac_SCT)[Annotation(mome_atac_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1) # restrict fixes issues with negative genome coordinates
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)
closest_f = ClosestFeature(mome_atac_SCT, regions = rownames(mome_atac_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)
for(tt in unique(closest_f$type)){
p = closest_f$query_region[closest_f$type==tt]
i = paste0("ATAC_", tt)
mome_atac_SCT[[i]] = CreateChromatinAssay(mome_atac_SCT@assays$ATAC@counts[p,])
mome_atac_SCT = RunTFIDF(mome_atac_SCT, assay = i)
mome_atac_SCT = FindTopFeatures(mome_atac_SCT, assay=i, min.cutoff = 300)
print(length(mome_atac_SCT[[i]]@var.features))
}Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_distal_ to atacdistal_Performing TF-IDF normalization
Some features contain 0 total counts[1] 10072Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_genebody_ to atacgenebody_Performing TF-IDF normalization
Some features contain 0 total counts[1] 18224Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_promoter_ to atacpromoter_Performing TF-IDF normalization
Some features contain 0 total counts[1] 7039# MACS3 peaks
DefaultAssay(mome_macs_SCT) = "ATAC"
transc = Annotation(mome_macs_SCT)[Annotation(mome_macs_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1)
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)
closest_f = ClosestFeature(mome_macs_SCT, regions = rownames(mome_macs_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)
for(tt in unique(closest_f$type)){
p = closest_f$query_region[closest_f$type==tt]
i = paste0("ATAC_", tt)
mome_macs_SCT[[i]] = CreateChromatinAssay(mome_macs_SCT@assays$ATAC@counts[p,])
mome_macs_SCT = RunTFIDF(mome_macs_SCT, assay = i)
mome_macs_SCT = FindTopFeatures(mome_macs_SCT, assay=i, min.cutoff = 200)
print(length(mome_macs_SCT[[i]]@var.features))
}Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_promoter_ to atacpromoter_Performing TF-IDF normalization[1] 10411Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_distal_ to atacdistal_Performing TF-IDF normalization
Some features contain 0 total counts[1] 13009Keys should be one or more alphanumeric characters followed by an underscore, setting key from atac_genebody_ to atacgenebody_Performing TF-IDF normalization
Some features contain 0 total counts[1] 15321Plotting
set.seed(1)
# create MOFA objectrun_mofa
mofa = create_mofa(mome_atac_SCT, assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))Creating MOFA object from a Seurat object...
No features specified, using variable features from the Seurat object...mofaUntrained MOFA model with the following characteristics:
Number of views: 4
Views names: RNA ATAC_promoter ATAC_distal ATAC_genebody
Number of features (per view): 2000 7039 10072 18224
Number of groups: 1
Groups names: group1
Number of samples (per group): 20484 # define model options
model_opts = get_default_model_options(mofa)
model_opts$num_factors = 25
mofa = prepare_mofa(mofa,model_options = model_opts)Some view(s) have a lot of features, it is recommended to performa more stringent feature selection before creating the MOFA object....Checking data options...
No data options specified, using default...
No training options specified, using default...
Checking model options...# run mofa (SLOW!)
mofa = run_mofa(mofa, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa",
use_basilisk = TRUE)Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa already exists, it will be replaced
Connecting to the mofapy2 package using basilisk.
Set 'use_basilik' to FALSE if you prefer to manually set the python binary using 'reticulate'.
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='RNA' group='group1' with N=20484 samples and D=2000 features...
Successfully loaded view='ATAC_promoter' group='group1' with N=20484 samples and D=7039 features...
Successfully loaded view='ATAC_distal' group='group1' with N=20484 samples and D=10072 features...
Successfully loaded view='ATAC_genebody' group='group1' with N=20484 samples and D=18224 features...
Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (RNA): gaussian
- View 1 (ATAC_promoter): gaussian
- View 2 (ATAC_distal): gaussian
- View 3 (ATAC_genebody): gaussian
######################################
## Training the model with seed 42 ##
######################################
ELBO before training: -11499617444.44
Iteration 1: time=622.61, ELBO=-978832718.02, deltaELBO=10520784726.418 (91.48812799%), Factors=25
Iteration 2: time=630.32, ELBO=-976416737.45, deltaELBO=2415980.575 (0.02100923%), Factors=25
Iteration 3: time=637.46, ELBO=-976126405.08, deltaELBO=290332.361 (0.00252471%), Factors=25
Iteration 4: time=665.97, ELBO=-975986573.20, deltaELBO=139831.882 (0.00121597%), Factors=25
Iteration 5: time=2951.07, ELBO=-975878348.30, deltaELBO=108224.904 (0.00094112%), Factors=25
Iteration 6: time=3582.39, ELBO=-975814351.91, deltaELBO=63996.391 (0.00055651%), Factors=25
Iteration 7: time=3464.41, ELBO=-975769593.72, deltaELBO=44758.188 (0.00038921%), Factors=25
Iteration 8: time=1722.38, ELBO=-975731005.42, deltaELBO=38588.300 (0.00033556%), Factors=25
Converged!
#######################
## Training finished ##
#######################
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa already exists, it will be replaced
Saving model in /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa...Factor(s) 2 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.saveRDS(mofa, file = "data/processed/multiome/MOFA_multiome_integATAC_SCT.RDS")Multiome factor analysis - MACS peaks
plot_dimred(mofa, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
#plot_dimred(mofa, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05,
# dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEIS2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEX3A", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)Error in .set_colorby(object, color_by) :
'color_by' was specified but it was not recognised, please read the documentationPlotting
set.seed(1)
# create MOFA object
mofa_macs = create_mofa(mome_macs_SCT,
assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))Creating MOFA object from a Seurat object...
No features specified, using variable features from the Seurat object...mofa_macsUntrained MOFA model with the following characteristics:
Number of views: 4
Views names: RNA ATAC_promoter ATAC_distal ATAC_genebody
Number of features (per view): 2000 10411 13009 15321
Number of groups: 1
Groups names: group1
Number of samples (per group): 20735 # define model options
model_opts = get_default_model_options(mofa_macs)
model_opts$num_factors = 25
mofa_macs = prepare_mofa(mofa_macs, model_options = model_opts)Some view(s) have a lot of features, it is recommended to performa more stringent feature selection before creating the MOFA object....Checking data options...
No data options specified, using default...
No training options specified, using default...
Checking model options...# run mofa (SLOW!)
mofa_macs = run_mofa(mofa_macs, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs",
use_basilisk = TRUE)Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs already exists, it will be replaced
Connecting to the mofapy2 package using basilisk.
Set 'use_basilik' to FALSE if you prefer to manually set the python binary using 'reticulate'.
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='RNA' group='group1' with N=20735 samples and D=2000 features...
Successfully loaded view='ATAC_promoter' group='group1' with N=20735 samples and D=10411 features...
Successfully loaded view='ATAC_distal' group='group1' with N=20735 samples and D=13009 features...
Successfully loaded view='ATAC_genebody' group='group1' with N=20735 samples and D=15321 features...
Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (RNA): gaussian
- View 1 (ATAC_promoter): gaussian
- View 2 (ATAC_distal): gaussian
- View 3 (ATAC_genebody): gaussian
######################################
## Training the model with seed 42 ##
######################################
ELBO before training: -12660486242.68
Iteration 1: time=707.61, ELBO=-1085560945.03, deltaELBO=11574925297.658 (91.42559832%), Factors=25
Iteration 2: time=724.01, ELBO=-1082174708.87, deltaELBO=3386236.151 (0.02674649%), Factors=25
Iteration 3: time=681.47, ELBO=-1081822947.07, deltaELBO=351761.800 (0.00277842%), Factors=25
Iteration 4: time=668.89, ELBO=-1081633322.20, deltaELBO=189624.874 (0.00149777%), Factors=25
Iteration 5: time=704.67, ELBO=-1081492873.24, deltaELBO=140448.958 (0.00110935%), Factors=25
Iteration 6: time=672.82, ELBO=-1081403049.17, deltaELBO=89824.077 (0.00070948%), Factors=25
Iteration 7: time=689.92, ELBO=-1081321850.90, deltaELBO=81198.270 (0.00064135%), Factors=25
Iteration 8: time=714.67, ELBO=-1081259560.55, deltaELBO=62290.342 (0.00049201%), Factors=25
Iteration 9: time=696.50, ELBO=-1081205771.84, deltaELBO=53788.717 (0.00042486%), Factors=25
Converged!
#######################
## Training finished ##
#######################
Warning: Output file /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs already exists, it will be replaced
Saving model in /local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs...1 factors were found to explain little or no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = F)
Factor(s) 2 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
The model contains highly correlated factors (see `plot_factor_cor(MOFAobject)`).
We recommend that you train the model with less factors and that you let it train for a longer time.saveRDS(mofa_macs, file = "data/processed/multiome/MOFA_multiome_integMACS_SCT.RDS")Add MOFA projection multiome data
# variance explained
plot_variance_explained(mofa_macs, plot_total = TRUE)[[1]]
[[2]]
samples_metadata(mofa_macs) = mome_macs_SCT@meta.data %>%
tibble::rownames_to_column("sample") %>%
as.data.table
# correlated technical variables
correlate_factors_with_covariates(mofa_macs,
covariates = c("nFeature_RNA","nFeature_ATAC",
"nCount_RNA","nCount_ATAC", "animal"))There are non-numeric values in the covariates data.frame, converting to numeric...
# example factors
plot_factor(mofa_macs, factors=1, group_by = "animal", color_by = "animal") 
plot_factor(mofa_macs, factors=10, group_by = "animal", color_by = "animal")
# example factor weights
plot_weights(mofa_macs, view = "RNA", factors = 1, nfeatures = 20, text_size = 4)
# select factors and run UMAP
factors = 1:get_dimensions(mofa)[["K"]]
factors = factors[c(3:6, 8, 11:get_dimensions(mofa_macs)[["K"]])]
mofa_macs = run_umap(mofa_macs, factors = factors, n_neighbors = 15, min_dist = 0.30)
# plots
plot_dimred(mofa_macs, method = "UMAP", color_by = "animal", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "nCount_RNA", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "seurat_clusters", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
# markers
plot_dimred(mofa_macs, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05,
dot_size = 1, legend = T)Error in .set_colorby(object, color_by) :
'color_by' was specified but it was not recognised, please read the documentationSave data
mome_macs_SCT_mofa = add_mofa_factors_to_seurat(mofa_macs, mome_macs_SCT, views = "all", factors = "all")
(1) Adding the MOFA factors to the 'reductions' slot of the default Seurat assay with the 'MOFA' key (no feature weights/loadings provided)...
No assay specified, setting assay as RNA by default.(2) Adding the UMAP representation obtained with the MOFA factors to the 'reductions' slot of the default Seurat assay using the key 'MOFAUMAP'...
No assay specified, setting assay as RNA by default.DefaultAssay(mome_macs_SCT_mofa) = "RNA"
mome_macs_SCT_mofa@reductions$MOFA@assay.used = "RNA"
mome_macs_SCT_mofa@reductions$MOFAUMAP@assay.used = "RNA"---
title: "Multiome analysis"
output: html_notebook
---


https://satijalab.org/signac/articles/merging.html
https://satijalab.org/signac/articles/integrate_atac.html
https://satijalab.org/signac/articles/pbmc_multiomic.html

 - merge peaks from all samples
 - recount
 - merge
 - (if necessary) integrate
 - motif enrichment analysis
 - TF footprint
 
 - load RNA-seq
 - match cell barcodes - actually, cellranger-arc gives to GEX and ATAC the GEX barcodes
 - do integrated processing
   - joint UMAP - done with WNN integration
   - gene-peak connection
   - multiome GRN
   - MOFA 2
 
 - co-accessibility analysis - not done, requires Monocle 3
 


# General Setup
Setup chunk

```{r, setup, include=FALSE}
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")
```

Setup reticulate

```{r}
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)
use_python(Sys.which("python"))
py_config()
```

Load libraries

```{r}
library(Signac)
# as of June 2021, Seurat still needs to be installed like this for ATAC integration:
## remotes::install_github(repo = 'satijalab/seurat', ref = 'develop')
library(Seurat)
library(GenomicRanges)
library(data.table)

# this (bespoke) package hosts the axolotl genome
# package is very large, cannot be installed in home directory
# and should be in a fast disk
library(BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M, 
        lib.loc = "/local1/USERS/tomasgomes/multiome_analysis/")

# Motif analysis
library(JASPAR2020)
library(TFBSTools)
library(motifmatchr)
library(chromVAR)

# multiome GRN
library(Pando)
library(foreach) # needed for Pando
library(igraph) # for Pando network analysis

# multiome factor analysis
library(MOFA2)
source("scripts/MOFA_toSeurat.R") #might not be needed with MOFA2 dev version

# getting GTF
# remotes::install_github("alyssafrazee/ballgown")
library(ballgown)

# plotting
library(ggplot2)
library(ggnetwork)
library(ggrepel)
library(patchwork)

# parallel processing
#library(future)
#plan("multiprocess", workers = 12)
#options(future.globals.maxSize = 100000 * 1024^2) # for 100 Gb RAM

set.seed(23)
```



# scATAC
## Gene setup
Mitochondrial genes

```{r}
mtgenes = c("COX1", "COX2", "COX3", "ATP6", "ND1", "ND5", "CYTB", "ND2", "ND4",
            "ATP8", "MT-CO1", "COI", "LOC9829747")
mtgenes = c(mtgenes, paste0("MT", mtgenes), paste0("MT-", mtgenes))

# in axolotl, MT genes are assembled within the genome, and also annotation is not 100% clear
# all these tricks are necessary because some regions make "ClosestFeature" crash
# get MT gene_id
basedir = "/links/groups/treutlein/USERS/tomasgomes/gene_refs/axolotl/Amex_T_v47/"
gene_name_match = read.table(paste0(basedir, "AmexT_v47.FULL_t2g_note.txt"), sep = "\t")[,2:3]
mtid = unique(gene_name_match[gene_name_match$V3 %in% mtgenes,1])
l_gtf = lapply(mtid, function(x) grepl(x, granges_axolotl$gene_id))
l_gtf = Reduce(cbind, l_gtf)
mtcoord = rowSums(l_gtf)>0
chrmt = unique(seqnames(granges_axolotl[mtcoord,]))
```

Read in axolotl features

```{r}
gtf_axolotl = "/local1/USERS/tomasgomes/multiome_analysis/AmexT_v47.FULL_corr_chr_cut.gtf"
granges_axolotl = ballgown::gffReadGR(gtf_axolotl)
# adding a gene biotype, as that's necessary for TSS metaprofile
granges_axolotl$gene_biotype = "protein_coding"

# with need to add the "proper" gene name
basedir = "/links/groups/treutlein/USERS/tomasgomes/gene_refs/axolotl/Amex_T_v47/"
gene_name_match = read.table(paste0(basedir, "AmexT_v47.FULL_t2g_note.txt"), sep = "\t")[,2:3]
gene_name_match = gene_name_match[!duplicated(gene_name_match$V2), ]
rownames(gene_name_match) = gene_name_match$V2

newgenenames = gene_name_match[granges_axolotl$gene_id,2]
granges_axolotl$gene_name = newgenenames
```


## Load data
### CellRanger peaks
Read in data

```{r}
samples = c("a1_1", "a1_2", "a3_1", "a3_2")
#data_dir = "/links/groups/treutlein/USERS/tomasgomes/data/axolotl/"
data_dir = "/local1/USERS/tomasgomes/multiome_analysis/"

counts_l = list()
frags_l = list()
peaks_l = list()
for(s in samples){
  counts_l[[s]] = Read10X_h5(filename = paste0(data_dir, s, "_ARC/outs/filtered_feature_bc_matrix.h5"))
  frags_file = paste0(data_dir, s, "_ARC/outs/atac_fragments.tsv.gz")
  peaks = read.table(paste0(data_dir, s, "_ARC/outs/atac_peaks.bed"), 
                     col.names = c("chr", "start", "end"))
  peaks_l[[s]] = makeGRangesFromDataFrame(peaks)
  
  frags_l[[s]] = CreateFragmentObject(path = frags_file, cells = colnames(counts_l[[s]]$Peaks))
}
```

Merge peaks

```{r}
# Create a unified set of peaks to quantify in each dataset
combined.peaks = reduce(x = c(peaks_l$a1_1, peaks_l$a1_2, peaks_l$a3_1, peaks_l$a3_2))

# Filter out bad peaks based on length
peakwidths = width(combined.peaks)
combined.peaks = combined.peaks[peakwidths > 12]

# there is a problem with coordinates starting at 0 for some reason...
combined.peaks = restrict(combined.peaks, start = 1)
combined.peaks
```

Make all individual Seurats with common peaks

```{r}
srat_atac_l = list()
for(s in names(frags_l)){
  feat = FeatureMatrix(fragments = frags_l[[s]], features = combined.peaks, 
                       cells = colnames(counts_l[[s]]$Peaks))
  ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
                             min.features = 100, annotation = granges_axolotl)
  srat_atac_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
  srat_atac_l[[s]]$dataset = s
  srat_atac_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}
```

Getting MT peaks and calculating MT%

```{r}
# match regions with MT genes
allregions = rownames(srat_atac_l$a1_1)
ischrmt = lapply(chrmt, function(x) grepl(x, allregions))
ischrmt = Reduce(cbind, ischrmt)
ischrmt = rowSums(ischrmt)>0

# add MT%
for(n in names(srat_atac_l)){
  all_cl = ClosestFeature(srat_atac_l[[n]], regions = allregions[ischrmt])
  ## get MT regions
  mt_peaks = all_cl[all_cl$gene_id %in% mtid,"query_region"]
  srat_atac_l[[n]] = PercentageFeatureSet(srat_atac_l[[n]], features = mt_peaks, col.name = "mt_perc")
}
```

Nucleosome and TSS signal (serve as QC metrics)

```{r}
tss_atac_plts = list()
frag_atac_plts = list()
for(n in names(srat_atac_l)){
  # compute nucleosome signal score per cell
  srat_atac_l[[n]] = NucleosomeSignal(object = srat_atac_l[[n]])
  
  # compute TSS enrichment score per cell
  srat_atac_l[[n]] = TSSEnrichment(object = srat_atac_l[[n]], fast = FALSE)
  
  srat_atac_l[[n]]$high.tss = ifelse(srat_atac_l[[n]]$TSS.enrichment > 2, 'High', 'Low')
  tss_atac_plts[[n]] = TSSPlot(srat_atac_l[[n]], group.by = 'high.tss') + NoLegend()
  
  srat_atac_l[[n]]$nucleosome_group = ifelse(srat_atac_l[[n]]$nucleosome_signal > 4, 'NS > 2', 'NS < 4')
  # this is veeery slow (~30-40min)
  frag_atac_plts[[n]] = FragmentHistogram(object = srat_atac_l[[n]], group.by = 'nucleosome_group')
}
```

Save data

```{r}
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l_raw.RDS")
```

### MACS peaks
Peak calling with MACS

```{r}
macs_l = list()
for(n in names(srat_atac_l)){
  print(n)
  srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
  srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = 5)
  srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
  
  cordat = DepthCor(srat_atac_l[[n]], n = 30)$data
  dims_use = cordat$Component[abs(cordat$counts)<0.3]
  print(dims_use)
  srat_atac_l[[n]] = FindNeighbors(object = srat_atac_l[[n]], reduction = 'lsi', dims = dims_use, 
                                   force.recalc = T, graph.name = "thegraph")
  srat_atac_l[[n]] = FindClusters(object = srat_atac_l[[n]], verbose = FALSE, algorithm = 3, 
                                  graph.name = "thegraph", resolution = 2)
  
  macs_l[[n]] = CallPeaks(object = srat_atac_l[[n]], group.by = "thegraph_res.2",
                          macs2.path = "~/bin/miniconda3/envs/macs2env/bin/macs3",
                          effective.genome.size = 2.0e+10, verbose = F)
}
```

How do new peaks compare with the original?

```{r}
# Create a unified set of peaks to quantify in each dataset
combined_macs = reduce(x = c(macs_l$a1_1, macs_l$a1_2, macs_l$a3_1, macs_l$a3_2))

# Filter out bad peaks based on length
peakwidths = width(combined_macs)
combined_macs = combined_macs[peakwidths > 12]

# there is a problem with coordinates starting at 0 for some reason...
combined_macs = restrict(combined_macs, start = 1)
combined_macs

boxplot(x = list("MACS" = log10(width(combined_macs)), "Original" = log10(width(combined.peaks))))
```

Generate new counts and objects

```{r}
srat_macs_l = list()
for(s in names(frags_l)){
  print(s)
  feat = FeatureMatrix(fragments = frags_l[[s]], features = combined_macs, 
                       cells = colnames(counts_l[[s]]$Peaks))
  ass = CreateChromatinAssay(feat, fragments = frags_l[[s]], sep = c(":", "-"), min.cells = 0,
                             min.features = 100, annotation = granges_axolotl)
  srat_macs_l[[s]] = CreateSeuratObject(ass, assay = "ATAC")
  srat_macs_l[[s]]$dataset = s
  srat_macs_l[[s]]$animal = strsplit(s, "_")[[1]][1]
}
```

Getting MT peaks and calculating MT%

```{r}
# match regions with MT genes
allregions = rownames(srat_macs_l$a1_1)
ischrmt = lapply(chrmt, function(x) grepl(x, allregions))
ischrmt = Reduce(cbind, ischrmt)
ischrmt = rowSums(ischrmt)>0

for(n in names(srat_macs_l)){
  all_cl = ClosestFeature(srat_macs_l[[n]], regions = allregions[ischrmt])
  ## get MT regions
  mt_peaks = all_cl[all_cl$gene_id %in% mtid,"query_region"]
  srat_macs_l[[n]] = PercentageFeatureSet(srat_macs_l[[n]], features = mt_peaks, col.name = "mt_perc")
}
```

Nucleosome and TSS signal (serve as QC metrics)

```{r}
tss_macs_plts = list()
frag_macs_plts = list()
for(n in names(srat_macs_l)){
  # compute nucleosome signal score per cell
  srat_macs_l[[n]] = NucleosomeSignal(object = srat_macs_l[[n]])
  
  # compute TSS enrichment score per cell
  srat_macs_l[[n]] = TSSEnrichment(object = srat_macs_l[[n]], fast = FALSE)
  
  srat_macs_l[[n]]$high.tss = ifelse(srat_macs_l[[n]]$TSS.enrichment > 2, 'High', 'Low')
  tss_macs_plts[[n]] = TSSPlot(srat_macs_l[[n]], group.by = 'high.tss') + NoLegend()
  
  srat_macs_l[[n]]$nucleosome_group = ifelse(srat_macs_l[[n]]$nucleosome_signal > 4, 'NS > 2', 'NS < 4')
  # this is veeery slow (~30-40min)
  frag_macs_plts[[n]] = FragmentHistogram(object = srat_macs_l[[n]], group.by = 'nucleosome_group')
}
```

Save data

```{r}
saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l_raw.RDS")
```


## Quality Control
Plotting QC metrics

```{r}
pdf("results/multiome/vln_qc_ATAC.pdf", width = 14, height = 4)
vln_atac_plts = list()
for(n in names(srat_atac_l)){
  vln_atac_plts[[n]] = VlnPlot(object = srat_atac_l[[n]], pt.size = 0.1, ncol = 5, log = T,
                               features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
                                            'TSS.enrichment', 'nucleosome_signal'))
  print(vln_atac_plts[[n]])
}
dev.off()

pdf("results/multiome/vln_qc_MACS.pdf", width = 14, height = 4)
vln_macs_plts = list()
for(n in names(srat_macs_l)){
  vln_macs_plts[[n]] = VlnPlot(object = srat_macs_l[[n]], pt.size = 0.1, ncol = 5, log = T,
                               features = c('nCount_ATAC', 'nFeature_ATAC', "mt_perc",
                                            'TSS.enrichment', 'nucleosome_signal'))
  print(vln_macs_plts[[n]])
}
dev.off()
```

Removing cells based on QC metrics

```{r}
# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(12000, 300, 12000, 300, 2, 3, 2),
               c(12000, 300, 12000, 300, 2, 3, 2),
               c(12000, 300, 12000, 300, 2, 3, 2),
               c(12000, 300, 12000, 300, 2, 3, 2))
names(qc_list) = names(srat_atac_l)
for(n in names(srat_atac_l)){
  srat_atac_l[[n]] = subset(x = srat_atac_l[[n]], 
                            subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
                              nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
                              nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
                              mt_perc<qc_list[[n]][7])
}
srat_atac_l

# this filtering is done per dataset, since different datasets can have different distributions
qc_list = list(c(10000, 200, 10000, 200, 2, 3, 5),
               c(10000, 200, 10000, 200, 2, 3, 5),
               c(10000, 200, 10000, 200, 2, 3, 5),
               c(10000, 200, 10000, 200, 2, 3, 5))
names(qc_list) = names(srat_macs_l)
for(n in names(srat_macs_l)){
  srat_macs_l[[n]] = subset(x = srat_macs_l[[n]], 
                            subset = nCount_ATAC<qc_list[[n]][1] & nCount_ATAC>qc_list[[n]][2] &
                              nFeature_ATAC<qc_list[[n]][3] & nFeature_ATAC>qc_list[[n]][4] &
                              nucleosome_signal<qc_list[[n]][5] & TSS.enrichment>qc_list[[n]][6] &
                              mt_perc<qc_list[[n]][7])
}
srat_macs_l
```


## Combine and analyse
Combine all ATAC data

```{r}
# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_atac_l)){
  cn = unlist(lapply(strsplit(colnames(srat_atac_l[[n]]), "-", fixed = T), function(x) x[1]))
  srat_atac_l[[n]] = RenameCells(srat_atac_l[[n]], 
                                 new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}

# merging
srat_atac_all = Reduce(merge, srat_atac_l)

# renaming cells - will have to be accounted for when adding the RNA!
for(n in names(srat_macs_l)){
  cn = unlist(lapply(strsplit(colnames(srat_macs_l[[n]]), "-", fixed = T), function(x) x[1]))
  srat_macs_l[[n]] = RenameCells(srat_macs_l[[n]], 
                                 new.names = paste0(cn, "-", gsub("_", "-", n, fixed = T)))
}

# merging
srat_macs_all = Reduce(merge, srat_macs_l)
```

Check how the data looks between samples

```{r}
CoveragePlot(object = srat_atac_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
CoveragePlot(object = srat_macs_all, group.by = 'dataset', region = "chr2ps2-334100000-334140000")
```


## Processing
Processing ATAC-seq data

```{r}
srat_atac_all = RunTFIDF(srat_atac_all)
srat_atac_all = FindTopFeatures(srat_atac_all, min.cutoff = 5)
srat_atac_all = RunSVD(srat_atac_all)

srat_macs_all = RunTFIDF(srat_macs_all)
srat_macs_all = FindTopFeatures(srat_macs_all, min.cutoff = 5)
srat_macs_all = RunSVD(srat_macs_all)
```

Check correlation of LSI with sequencing depth (since often times the first component is technical var)

```{r}
DepthCor(srat_atac_all, n = 30)
DepthCor(srat_macs_all, n = 30)
```

Get UMAP

```{r}
cordat = DepthCor(srat_atac_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_atac_all = RunUMAP(object = srat_atac_all, reduction = 'lsi', dims = dims_use, verbose = F)
DimPlot(object = srat_atac_all, group.by = "dataset")
FeaturePlot(object = srat_atac_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)

cordat = DepthCor(srat_macs_all, n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
srat_macs_all = RunUMAP(object = srat_macs_all, reduction = 'lsi', dims = dims_use, verbose = F)
DimPlot(object = srat_macs_all, group.by = "dataset")
FeaturePlot(object = srat_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```


## Integration
Processing for each ATAC-seq sample

```{r}
for(n in names(srat_atac_l)){
  srat_atac_l[[n]] = RunTFIDF(srat_atac_l[[n]])
  srat_atac_l[[n]] = FindTopFeatures(srat_atac_l[[n]], min.cutoff = "q0")
  srat_atac_l[[n]] = RunSVD(srat_atac_l[[n]])
}
DepthCor(srat_atac_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_atac_l$a3_2, reduction = "lsi", n = 30)

for(n in names(srat_macs_l)){
  srat_macs_l[[n]] = RunTFIDF(srat_macs_l[[n]])
  srat_macs_l[[n]] = FindTopFeatures(srat_macs_l[[n]], min.cutoff = "q0")
  srat_macs_l[[n]] = RunSVD(srat_macs_l[[n]])
}
DepthCor(srat_macs_l$a1_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a1_2, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_1, reduction = "lsi", n = 30)
DepthCor(srat_macs_l$a3_2, reduction = "lsi", n = 30)
```

Integrate based on each experiment - CellRanger peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_l, reduction = "rlsi", dims = 2:30,
                                             anchor.features = rownames(srat_atac_all))

# integrate LSI embeddings
integ_all = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
                                 new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)

DepthCor(integ_all, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_all = RunUMAP(integ_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_all, group.by = "dataset")
FeaturePlot(object = integ_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Integrate based on each experiment - MACS3 peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_l, reduction = "rlsi", dims = 2:30,
                                             anchor.features = rownames(srat_macs_all))

# integrate LSI embeddings
integ_macs_all = IntegrateEmbeddings(anchorset = integration.anchors, 
                                     reductions = srat_macs_all[["lsi"]],
                                     new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)

DepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_all, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_all = RunUMAP(integ_macs_all, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_all, group.by = "dataset")
FeaturePlot(object = integ_macs_all, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Processing ATAC-seq by animal

```{r}
srat_atac_animal = SplitObject(srat_atac_all, split.by = "animal")

for(n in names(srat_atac_animal)){
  srat_atac_animal[[n]] = RunTFIDF(srat_atac_animal[[n]])
  srat_atac_animal[[n]] = FindTopFeatures(srat_atac_animal[[n]], min.cutoff = "q0")
  srat_atac_animal[[n]] = RunSVD(srat_atac_animal[[n]])
}
DepthCor(srat_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_atac_animal$a3, reduction = "lsi", n = 30)

srat_macs_animal = SplitObject(srat_macs_all, split.by = "animal")

for(n in names(srat_macs_animal)){
  srat_macs_animal[[n]] = RunTFIDF(srat_macs_animal[[n]])
  srat_macs_animal[[n]] = FindTopFeatures(srat_macs_animal[[n]], min.cutoff = "q0")
  srat_macs_animal[[n]] = RunSVD(srat_macs_animal[[n]])
}
DepthCor(srat_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(srat_macs_animal$a3, reduction = "lsi", n = 30)
```

Integrate based on each animal - CellRanger peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_atac_animal, 
                                             reduction = "rlsi", dims = c(2:4,6:30),
                                             anchor.features = rownames(srat_atac_all))

# integrate LSI embeddings
integ_animal = IntegrateEmbeddings(anchorset = integration.anchors, reductions = srat_atac_all[["lsi"]],
                                   new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)

DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_animal = RunUMAP(integ_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_animal, group.by = "dataset")
FeaturePlot(object = integ_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Integrate based on each animal - MACS3 peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = srat_macs_animal, 
                                             reduction = "rlsi", dims = c(2:4,6:30),
                                             anchor.features = rownames(srat_macs_all))

# integrate LSI embeddings
integ_macs_animal = IntegrateEmbeddings(anchorset = integration.anchors, 
                                        reductions = srat_macs_all[["lsi"]],
                                        new.reduction.name = "integrated_lsi", dims.to.integrate = 1:30)

DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = RunUMAP(integ_macs_animal, reduction = "integrated_lsi", dims = dims_use, verbose = F)
DimPlot(integ_macs_animal, group.by = "dataset")
FeaturePlot(object = integ_macs_animal, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Saving data so far

```{r}
saveRDS(srat_atac_l, file = "data/processed/multiome/srat_atac_l.RDS")
saveRDS(srat_atac_animal, file = "data/processed/multiome/srat_atac_animal.RDS")
saveRDS(srat_atac_all, file = "data/processed/multiome/srat_atac_all.RDS")
saveRDS(integ_all, file = "data/processed/multiome/integ_all.RDS")
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal.RDS")

saveRDS(srat_macs_l, file = "data/processed/multiome/srat_macs_l.RDS")
saveRDS(srat_macs_animal, file = "data/processed/multiome/srat_macs_animal.RDS")
saveRDS(srat_macs_all, file = "data/processed/multiome/srat_macs_all.RDS")
saveRDS(integ_macs_all, file = "data/processed/multiome/integ_macs_all.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal.RDS")
```


## Clustering and annotation
We're going with the integration by animal
Find clusters

```{r}
cordat = DepthCor(integ_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_animal = FindNeighbors(object = integ_animal, reduction = 'integrated_lsi', dims = dims_use, 
                             force.recalc = T, graph.name = "thegraph")
integ_animal = FindClusters(object = integ_animal, verbose = FALSE, algorithm = 3, 
                            graph.name = "thegraph", resolution = 0.8)
DimPlot(object = integ_animal, label = TRUE) + NoLegend()

cordat = DepthCor(integ_macs_animal, reduction = "integrated_lsi", n = 30)$data
dims_use = cordat$Component[abs(cordat$counts)<0.3]
integ_macs_animal = FindNeighbors(object = integ_macs_animal, reduction = 'integrated_lsi', 
                                  dims = dims_use, 
                             force.recalc = T, graph.name = "thegraph")
integ_macs_animal = FindClusters(object = integ_macs_animal, verbose = FALSE, algorithm = 3, 
                                 graph.name = "thegraph", resolution = 0.8)
DimPlot(object = integ_macs_animal, label = TRUE) + NoLegend()
```

Get DE peaks

```{r}
allmk_ATAC = suppressWarnings(FindAllMarkers(object = integ_animal, min.pct = 0.2, test.use = 'LR', 
                            latent.vars = 'nCount_ATAC', verbose = T))
allmk_ATAC = allmk_ATAC[allmk_ATAC$p_val_adj<=0.05,]

allmk_macs = suppressWarnings(FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, test.use = 'LR', 
                            latent.vars = 'nCount_ATAC', verbose = T))
allmk_macs = allmk_macs[allmk_macs$p_val_adj<=0.05,]
```

Annotate said peaks

```{r}
reg = unique(allmk_ATAC$gene)
closest_allmk = ClosestFeature(integ_animal, regions = unique(reg))

allmk_ATAC = merge(allmk_ATAC, closest_allmk[,c("query_region","type","distance","gene_name")], 
                   by.x = "gene", by.y = "query_region", all.x = T)

write.csv(allmk_ATAC, file = "results/multiome/allmk_ATAC.csv", col.names = T, row.names = F, quote = F)

reg = unique(allmk_macs$gene)
closest_allmk_macs = ClosestFeature(integ_macs_animal, regions = unique(reg))

allmk_macs = merge(allmk_macs[,-c(8:10)],
                   closest_allmk_macs[,c("query_region","type","distance","gene_name")], 
                   by.x = "gene", by.y = "query_region", all.x = T)

write.csv(allmk_macs, file = "results/multiome/allmk_macs.csv", col.names = T, row.names = F, quote = F)
```

Example coverage plot

```{r}
CoveragePlot(object = integ_animal, region = "chr8ps1-430732645-430746183", 
             extend.upstream = 5000, extend.downstream = 5000)

CoveragePlot(object = integ_macs_animal, region = "chr8ps1-430732645-430746183", 
             extend.upstream = 5000, extend.downstream = 5000)
```


## Gene activities
Obtaining gene activities - helps give meaning to the detected peaks

```{r}
gene_act = GeneActivity(integ_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)
# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)

# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_animal = NormalizeData(object = integ_animal, assay = 'GeneAct', 
                             normalization.method = 'LogNormalize', 
                             scale.factor = median(integ_animal$nCount_GeneAct))

gene_act = GeneActivity(integ_macs_animal, max.width = NULL, extend.upstream = 5000, extend.downstream = 5000)
# collapse identical gene names
gn = rownames(gene_act)
gn = unlist(lapply(strsplit(gn, ".", fixed = T), function(x) x[1]))
gene_act_coll = rowsum(gene_act, group = gn)

# add the gene activity matrix to the Seurat object as a new assay and normalize it
integ_macs_animal[['GeneAct']] = CreateAssayObject(counts = gene_act_coll)
integ_macs_animal = NormalizeData(object = integ_macs_animal, assay = 'GeneAct', 
                                  normalization.method = 'LogNormalize', 
                                  scale.factor = median(integ_macs_animal$nCount_GeneAct))
```

Plotting gene activities and get DE genes

```{r}
DefaultAssay(integ_animal) = 'GeneAct'

FeaturePlot(object = integ_animal, features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
            pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)

allmk_geneact = FindAllMarkers(object = integ_animal, min.pct = 0.2, 
                               test.use = 'LR', latent.vars = 'nCount_GeneAct')
allmk_geneact = allmk_geneact[allmk_geneact$p_val_adj<=0.05,]

write.csv(allmk_geneact, file = "results/multiome/allmk_geneact.csv", 
          col.names = T, row.names = F, quote = F)

DefaultAssay(integ_macs_animal) = 'GeneAct'

FeaturePlot(object = integ_macs_animal, 
            features = c('GAD2', 'MEX3A', 'MEIS1', 'PDGFRA', 'SLC17A6', 'EPOR'),
            pt.size = 0.1, max.cutoff = 'q95', ncol = 3, order = F)

allmk_geneact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, 
                               test.use = 'LR', latent.vars = 'nCount_GeneAct')
allmk_geneact_macs = allmk_geneact_macs[allmk_geneact_macs$p_val_adj<=0.05,]

write.csv(allmk_geneact_macs, file = "results/multiome/allmk_geneact_macs.csv", 
          col.names = T, row.names = F, quote = F)
```

Save object with annotations and gene activities

```{r}
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters.RDS")
```


## Motif analysis
Add motif information to the ATAC object

```{r}
# Get a list of motif position frequency matrices from the JASPAR database - vertebrates
pfm = getMatrixSet(JASPAR2020, opts = list(tax_group = "vertebrates", all_versions = F))

DefaultAssay(integ_animal) = "ATAC"

# add motif information
integ_animal = AddMotifs(object = integ_animal, pfm = pfm, assay = "ATAC",
                         genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

DefaultAssay(integ_macs_animal) = "ATAC"

# add motif information
integ_macs_animal = AddMotifs(object = integ_macs_animal, pfm = pfm, assay = "ATAC",
                              genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)
```

Determine enriched Motifs from group of regions

```{r}
allmk_ATAC = FindAllMarkers(object = integ_animal, min.pct = 0.2, test.use = 'LR', 
                            latent.vars = 'nCount_ATAC', verbose = F)
allmk_ATAC = allmk_ATAC[allmk_ATAC$p_val_adj<=0.05,]

write.csv(allmk_ATAC, file = "results/multiome/allmk_motifs.csv", 
          col.names = T, row.names = F, quote = F)

# first compute the GC content for each peak
integ_animal = RegionStats(integ_animal, 
                           genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

enr_motifs_atac = FindMotifs(integ_animal, features = unique(allmk_ATAC$gene))

allmk_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, test.use = 'LR', 
                            latent.vars = 'nCount_ATAC', verbose = F)
allmk_macs = allmk_macs[allmk_macs$p_val_adj<=0.05,]

write.csv(allmk_macs, file = "results/multiome/allmk_motifs_macs.csv", 
          col.names = T, row.names = F, quote = F)

# first compute the GC content for each peak
integ_macs_animal = RegionStats(integ_macs_animal, 
                                genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

enr_motifs_macs = FindMotifs(integ_macs_animal, features = unique(allmk_macs$gene))
```

Plot top enriched motifs

```{r}
MotifPlot(integ_animal, motifs = head(rownames(enr_motifs_atac)))
MotifPlot(integ_macs_animal, motifs = head(rownames(enr_motifs_macs)))
```

Compute motif activity

```{r}
integ_animal = RunChromVAR(integ_animal,
                          genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

DefaultAssay(integ_animal) = 'chromvar'

# look at the activity of Mef2c
FeaturePlot(object = integ_animal, features = "MA1099.2", min.cutoff = 'q10', 
            max.cutoff = 'q90', pt.size = 0.1)

integ_macs_animal = RunChromVAR(integ_macs_animal,
                                genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

DefaultAssay(integ_macs_animal) = 'chromvar'

# look at the activity of Mef2c
FeaturePlot(object = integ_macs_animal, features = "MA1099.2", min.cutoff = 'q10', 
            max.cutoff = 'q90', pt.size = 0.1)
```

DE motifs by activity

```{r}
DefaultAssay(integ_animal) = 'chromvar'
diff_motifact = FindAllMarkers(object = integ_animal, min.pct = 0.2, test.use = 'LR', 
                               latent.vars = 'nCount_ATAC', verbose = F)
diff_motifact = diff_motifact[diff_motifact$p_val_adj<=0.05,]
diff_motifact$name = unlist(integ_animal@assays$ATAC@motifs@motif.names[diff_motifact$gene])

write.csv(diff_motifact, file = "results/multiome/diff_motifact_atac.csv", 
          col.names = T, row.names = F, quote = F)

MotifPlot(integ_animal, motifs = head(diff_motifact$gene), assay = 'ATAC')

DefaultAssay(integ_macs_animal) = 'chromvar'
diff_motifact_macs = FindAllMarkers(object = integ_macs_animal, min.pct = 0.2, test.use = 'LR',
                                    latent.vars = 'nCount_ATAC', verbose = F)
diff_motifact_macs = diff_motifact_macs[diff_motifact_macs$p_val_adj<=0.05,]
diff_motifact_macs$name = unlist(integ_macs_animal@assays$ATAC@motifs@motif.names[diff_motifact_macs$gene])

write.csv(diff_motifact_macs, file = "results/multiome/diff_motifact_macs.csv", 
          col.names = T, row.names = F, quote = F)

MotifPlot(integ_macs_animal, motifs = head(diff_motifact_macs$gene), assay = 'ATAC')
```

TF footprinting (needs access to fragments) - VERY SLOW, do for relevant ones only

```{r}
DefaultAssay(integ_animal) = "ATAC"

# gather the footprinting information for sets of motifs
## VERY LONG STEP
tfmotif = c("HES1")#c("Sox3", "HES1", "ZBTB14")
integ_animal = Footprint(object = integ_animal, motif.name = tfmotif,
                         in.peaks = T,
                         genome = BSgenome.Amexicanum.axolotlomics.AmexGv6cut500M)

# plot the footprint data for each group of cells
PlotFootprint(integ_animal, features = tfmotif)
```

Save object with annotations and gene activities

```{r}
saveRDS(integ_animal, file = "data/processed/multiome/integ_animal_clusters_motifs.RDS")
saveRDS(integ_macs_animal, file = "data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")
```



# Adding snRNA-seq
## Load data
Load ATAC data again

```{r}
integ_animal = readRDS("data/processed/multiome/integ_animal_clusters_motifs.RDS")
integ_macs_animal = readRDS("data/processed/multiome/integ_macs_animal_clusters_motifs.RDS")
```


Load all snRNA-seq (multiome and other)

```{r}
rna_dat = readRDS("data/expression/axolotl_reclust/all_nuclei_clustered_highlevel_anno.RDS")
```

Filter non-multiome data

```{r}
rna_dat = rna_dat[,rna_dat$chem=="multiome"]
```

Reformat cell names to match those in ATAC

```{r}
newcellnames = colnames(rna_dat)
newcellnames = gsub("-1_1", "-a1-1", newcellnames)
newcellnames = gsub("-1_2", "-a1-2", newcellnames)
newcellnames = gsub("-1_3", "-a3-1", newcellnames)
newcellnames = gsub("-1_4", "-a3-2", newcellnames)
rna_dat = RenameCells(rna_dat, new.names = newcellnames)
```

Overlap in cells with ATAC

```{r}
par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_animal)))
title("all")
for(s in c("a1-1","a1-2","a3-1","a3-2")){
  gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))], 
                    "ATAC" = colnames(integ_animal)[grepl(s,colnames(integ_animal))]))
  title(s)
}

par(mfrow = c(2,3), mar = c(0.7,0,0.7,0))
gplots::venn(list("RNA" = colnames(rna_dat), "ATAC" = colnames(integ_macs_animal)))
title("all")
for(s in c("a1-1","a1-2","a3-1","a3-2")){
  gplots::venn(list("RNA" = colnames(rna_dat)[grepl(s,colnames(rna_dat))], 
                    "ATAC" = colnames(integ_macs_animal)[grepl(s,colnames(integ_macs_animal))]))
  title(s)
}
```


## Normalise RNA data
Subset and SCT normalise

```{r}
sub_rna_atac = rna_dat[,colnames(rna_dat) %in% colnames(integ_animal)]
sub_rna_atac = NormalizeData(sub_rna_atac)
sub_rna_atac = FindVariableFeatures(sub_rna_atac)
sub_rna_atac = ScaleData(sub_rna_atac, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))


sub_rna_macs = rna_dat[,colnames(rna_dat) %in% colnames(integ_macs_animal)]
sub_rna_macs = NormalizeData(sub_rna_macs)
sub_rna_macs = FindVariableFeatures(sub_rna_macs)
sub_rna_macs = ScaleData(sub_rna_macs, vars.to.regress = c("nCount_RNA", "nFeature_RNA", "percent.mt"))
```

Get PCA and UMAP for RNA

```{r}
sub_rna_atac = RunPCA(sub_rna_atac, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_atac, ndims = 50)
sub_rna_atac = RunUMAP(sub_rna_atac, reduction = "pca", dims = 1:40, verbose = F)

sub_rna_macs = RunPCA(sub_rna_macs, verbose = FALSE, assay = "RNA")
ElbowPlot(sub_rna_macs, ndims = 50)
sub_rna_macs = RunUMAP(sub_rna_macs, reduction = "pca", dims = 1:40, verbose = F)
```

## Add RNA to ATAC data
Subset ATAC data

```{r}
atac_multiome = integ_animal[,colnames(integ_animal) %in% colnames(sub_rna_atac)]
macs_multiome = integ_macs_animal[,colnames(integ_macs_animal) %in% colnames(sub_rna_macs)]
```

Processing ATAC

```{r}
DefaultAssay(atac_multiome) = 'ATAC'

atac_multiome = RunTFIDF(atac_multiome)
atac_multiome = FindTopFeatures(atac_multiome, min.cutoff = "q0")
atac_multiome = RunSVD(atac_multiome)

DepthCor(atac_multiome, n = 50)

atac_multiome = RunUMAP(atac_multiome, reduction = "lsi", dims = c(2:4,6:30), verbose = F)
DimPlot(atac_multiome, group.by = "dataset")

DefaultAssay(macs_multiome) = 'ATAC'

macs_multiome = RunTFIDF(macs_multiome)
macs_multiome = FindTopFeatures(macs_multiome, min.cutoff = "q0")
macs_multiome = RunSVD(macs_multiome)

DepthCor(macs_multiome, n = 50)

macs_multiome = RunUMAP(macs_multiome, reduction = "lsi", dims = c(2:4,6:30), verbose = F)
DimPlot(macs_multiome, group.by = "dataset")
```

### Re-integrating ATAC-seq
Processing ATAC-seq by animal

```{r}
multiome_atac_animal = SplitObject(atac_multiome, split.by = "animal")

for(n in names(multiome_atac_animal)){
  multiome_atac_animal[[n]] = RunTFIDF(multiome_atac_animal[[n]])
  multiome_atac_animal[[n]] = FindTopFeatures(multiome_atac_animal[[n]], min.cutoff = "q0")
  multiome_atac_animal[[n]] = RunSVD(multiome_atac_animal[[n]])
}
DepthCor(multiome_atac_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_atac_animal$a3, reduction = "lsi", n = 30)

multiome_macs_animal = SplitObject(macs_multiome, split.by = "animal")

for(n in names(multiome_macs_animal)){
  multiome_macs_animal[[n]] = RunTFIDF(multiome_macs_animal[[n]])
  multiome_macs_animal[[n]] = FindTopFeatures(multiome_macs_animal[[n]], min.cutoff = "q0")
  multiome_macs_animal[[n]] = RunSVD(multiome_macs_animal[[n]])
}
DepthCor(multiome_macs_animal$a1, reduction = "lsi", n = 30)
DepthCor(multiome_macs_animal$a3, reduction = "lsi", n = 30)
```

Integrate based on each animal - CellRanger peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_atac_animal, 
                                             reduction = "rlsi", dims = c(2:4,6:30),
                                             anchor.features = rownames(multiome_atac_animal))

# integrate LSI embeddings
mome_atac = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
                                reductions = atac_multiome[["lsi"]],
                                new.reduction.name = "integrated_lsi")

DepthCor(mome_atac, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
mome_atac = RunUMAP(mome_atac, reduction = "integrated_lsi", dims = c(2:4,6:30), verbose = F)
DimPlot(mome_atac, group.by = "dataset")
FeaturePlot(object = mome_atac, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Integrate based on each animal - MACS peaks

```{r}
# find integration anchors
integration.anchors = FindIntegrationAnchors(object.list = multiome_macs_animal, 
                                             reduction = "rlsi", dims = c(2:3,6:30),
                                             anchor.features = rownames(multiome_macs_animal))

# integrate LSI embeddings
mome_macs = IntegrateEmbeddings(anchorset = integration.anchors, dims.to.integrate = 1:30,
                                reductions = macs_multiome[["lsi"]],
                                new.reduction.name = "integrated_lsi")

DepthCor(mome_macs, reduction = "integrated_lsi", n = 30)

# create a new UMAP using the integrated embeddings
mome_macs = RunUMAP(mome_macs, reduction = "integrated_lsi", dims = c(2:3,5:30), verbose = F)
DimPlot(mome_macs, group.by = "dataset")
FeaturePlot(object = mome_macs, features = c('nCount_ATAC', "nFeature_ATAC", "mt_perc"),
            pt.size = 0.1, ncol = 2)
```

Add RNA to ATAC

```{r}
mome_atac[["RNA"]] = sub_rna_atac@assays$RNA
mome_atac@reductions$pca = sub_rna_atac@reductions$pca

mome_macs[["RNA"]] = sub_rna_macs@assays$RNA
mome_macs@reductions$pca = sub_rna_macs@reductions$pca
```

Save multiome data

```{r}
saveRDS(mome_atac, file = "data/processed/multiome/multiome_integATAC_SCT.RDS")
saveRDS(mome_macs, file = "data/processed/multiome/multiome_integMACS_SCT.RDS")
```

Plot ATAC with regions and cellclusters

```{r}
dir = "/links/groups/treutlein/USERS/tomasgomes/projects/pallium_evo/"

# read data 
mome_atac_SCT = readRDS(file = paste0(dir,
                                      "data/processed/multiome/multiome_integATAC_SCT.RDS"))

# annotations
## cell type annotations
meta = read.csv(paste0(dir,"data/annotations/axolotl_all_umeta.csv"), 
                header = T, row.names = 1)
### these cell names need reformatting
newcellnames = rownames(meta)
newcellnames = gsub("-1_1", "-a1-1", newcellnames)
newcellnames = gsub("-1_2", "-a1-2", newcellnames)
newcellnames = gsub("-1_3", "-a3-1", newcellnames)
newcellnames = gsub("-1_4", "-a3-2", newcellnames)
rownames(meta) = newcellnames
mome_atac_SCT = AddMetaData(mome_atac_SCT, metadata = meta)

## predicted regions (top is used)
meta_reg = read.csv(paste0(dir,"data/processed/multiome/WP_region_predictions.csv"), 
                    header = T, row.names = 1)
mome_atac_SCT = AddMetaData(mome_atac_SCT, metadata = meta_reg)


cols_cc = c(
#epen
"#12400c", "#2d6624","#1d4f15", "#174711", "#2d6624", "#3d7f33", "#3b7b30", "#468b3b", "#4f9843","#5dae50", "#66bb58", "#72cd64", "#306a26", "#78d669", "#81e472",
#gaba
"#700209", "#75090e","#7a0f13", "#801517", "#851a1b", "#8a1f1f", "#902423", "#952927", "#9a2d2c","#a03230", "#a53634", "#aa3a39", "#b03f3d","#b54342", "#ba4846", "#c04c4b", "#c5504f", "#ca5554", "#d05959", "#d55e5e","#73050c", "#780c11","#8d2221", "#982b2a","#a23432", "#a83837", "#b2413f", "#b84544", "#bd4a49", "#c85352", #"#cd5756",
#glut
"#054674", "#134d7b","#1d5481", "#265a88", "#2e618e", "#73a4cb", "#366995", "#3e709c", "#4677a2","#4d7ea9", "#5586b0", "#5c8db7", "#6495bd","#6b9cc4", "#7bacd2", "#8ebfe4", "#96c7eb", "#9ecff2", "#18507e", "#18507e","#2a5e8b", "#497ba6","#5889b3", "#6fa0c8","#7fafd6", "#6091ba", "#5182ac", "#3a6c98", "#a6d7f9",
#npc
"#ffb120", "#feb72a","#fdbc34", "#fcc13d", "#fbc745", "#facc4e", "#f9d156", "#f8d65f", "#f8da68","#f7df70", "#f7e479", "#f7e882", "#f7ed8a", "#f7f193", "#eca319"
)
ccnames = unique(sort(meta$cellclusters))
names(cols_cc) = c(ccnames[grepl("epen", ccnames)], ccnames[grepl("GABA", ccnames)],ccnames[grepl("glut", ccnames)],ccnames[grepl("npc", ccnames)])

more_cols = c("#712166","#B0279D", "#BE5AB0",   "#E43D88", "#F662A5",   "#E6530D")
names(more_cols) = c("endothelial_11","endothelial_12","endothelial_14",
                     "oligodendrocyte_10","oligodendrocyte_15","microglia_8")

reg_cols = c("other/unknown_pred" = "#C7CCC7", 
             "medial" = "#52168D", "medial_pred" = "#661CB0", 
             "dorsal" = "#C56007", "dorsal_pred" = "#ED7307", 
             "lateral" = "#118392", "lateral_pred" = "#16A3B6")
reg_cols_simp = c("medial" = "#52168D", "dorsal" = "#C56007", "lateral" = "#118392")




# plotting most important labels
pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_cellclusters.pdf", height = 3, width = 3.2)
DimPlot(mome_atac_SCT, reduction = "umap", group.by = "cellclusters", 
        pt.size = 0.000000001, shuffle = T)+
  scale_colour_manual(values = c(cols_cc, more_cols))+
  NoAxes()+NoLegend()+
  theme(aspect.ratio = 1, plot.title = element_blank())
dev.off()

pdf("results/ATAC_axolotl_features/UMAP_multiome_ATAC_only_region.pdf", height = 3, width = 3.2)
DimPlot(mome_atac_SCT, reduction = "umap", group.by = "pred_regions_top", 
        pt.size = 0.000000001, shuffle = T)+
  scale_colour_manual(values = reg_cols_simp)+
  NoAxes()+NoLegend()+
  theme(aspect.ratio = 1, plot.title = element_blank())
dev.off()
```




## Integrated processing
Load multiome data

```{r}
mome_atac_SCT = readRDS(file = "data/processed/multiome/multiome_integATAC_SCT.RDS")
mome_macs_SCT = readRDS(file = "data/processed/multiome/multiome_integMACS_SCT.RDS")
```

### Combined projections
Prepare segregated peaks. This is done using the annotation based on the original GTF file

```{r}
# CellRanger peaks
DefaultAssay(mome_atac_SCT) = "ATAC"
transc = Annotation(mome_atac_SCT)[Annotation(mome_atac_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1) # restrict fixes issues with negative genome coordinates
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)

closest_f = ClosestFeature(mome_atac_SCT, regions = rownames(mome_atac_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)

for(tt in unique(closest_f$type)){
  p = closest_f$query_region[closest_f$type==tt]
  i = paste0("ATAC_", tt)
  mome_atac_SCT[[i]] = CreateChromatinAssay(mome_atac_SCT@assays$ATAC@counts[p,])
  mome_atac_SCT = RunTFIDF(mome_atac_SCT, assay = i)
  mome_atac_SCT = FindTopFeatures(mome_atac_SCT, assay=i, min.cutoff = 300)
  print(length(mome_atac_SCT[[i]]@var.features))
}

# MACS3 peaks
DefaultAssay(mome_macs_SCT) = "ATAC"
transc = Annotation(mome_macs_SCT)[Annotation(mome_macs_SCT)$type=="transcript",] # get transcripts
transc = granges(transc[width(transc)>600,])
proms = restrict(promoters(transc, upstream=5000, downstream=500), start = 1)
proms$type = "promoter"
genebodies = setdiff(transc, proms)
genebodies$type = "genebody"
feat = c(proms, genebodies)

closest_f = ClosestFeature(mome_macs_SCT, regions = rownames(mome_macs_SCT), annotation = feat)
closest_f$type = ifelse(closest_f$distance!=0, "distal", closest_f$type)

for(tt in unique(closest_f$type)){
  p = closest_f$query_region[closest_f$type==tt]
  i = paste0("ATAC_", tt)
  mome_macs_SCT[[i]] = CreateChromatinAssay(mome_macs_SCT@assays$ATAC@counts[p,])
  mome_macs_SCT = RunTFIDF(mome_macs_SCT, assay = i)
  mome_macs_SCT = FindTopFeatures(mome_macs_SCT, assay=i, min.cutoff = 200)
  print(length(mome_macs_SCT[[i]]@var.features))
}
```

Multiome factor analysis - CellRanger peaks

```{r}
set.seed(1)
# create MOFA objectrun_mofa
mofa = create_mofa(mome_atac_SCT, assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))
mofa

# define model options
model_opts = get_default_model_options(mofa)
model_opts$num_factors = 25
mofa = prepare_mofa(mofa,model_options = model_opts)

# run mofa (SLOW!)
mofa = run_mofa(mofa, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa", 
                use_basilisk = TRUE)

saveRDS(mofa, file = "data/processed/multiome/MOFA_multiome_integATAC_SCT.RDS")
```

Plotting

```{r}
# variance explained
plot_variance_explained(mofa, plot_total = TRUE)

samples_metadata(mofa) = mome_atac_SCT@meta.data %>%
  tibble::rownames_to_column("sample") %>%
  as.data.table

# correlated technical variables
correlate_factors_with_covariates(mofa, covariates = c("nFeature_RNA","nFeature_ATAC",
                                                       "nCount_RNA","nCount_ATAC", "animal"))

# example factors
plot_factor(mofa, factors=1, group_by = "animal", color_by = "animal")
plot_factor(mofa, factors=11, group_by = "animal", color_by = "animal")

# example factor weights
plot_weights(mofa, view = "RNA", factors = 1, nfeatures = 20, text_size = 4)

# select factors and run UMAP
factors = 1:get_dimensions(mofa)[["K"]]
factors = factors[c(1, 3:6, 8, 10, 12:get_dimensions(mofa)[["K"]])]
mofa = run_umap(mofa, factors = factors, n_neighbors = 15,  min_dist = 0.30)

# plots
plot_dimred(mofa, method = "UMAP", color_by = "animal", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "nCount_RNA", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "seurat_clusters", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)

# markers
plot_dimred(mofa, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
#plot_dimred(mofa, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05, 
#            dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEIS2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "MEX3A", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa, method = "UMAP", color_by = "GLI2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
```

Multiome factor analysis - MACS peaks

```{r}
set.seed(1)
# create MOFA object
mofa_macs = create_mofa(mome_macs_SCT, 
                        assays = c("RNA","ATAC_promoter", "ATAC_distal", "ATAC_genebody"))
mofa_macs

# define model options
model_opts = get_default_model_options(mofa_macs)
model_opts$num_factors = 25
mofa_macs = prepare_mofa(mofa_macs, model_options = model_opts)

# run mofa (SLOW!)
mofa_macs = run_mofa(mofa_macs, outfile = "/local1/USERS/tomasgomes/multiome_analysis/tmp_mofa_macs", 
                     use_basilisk = TRUE)

saveRDS(mofa_macs, file = "data/processed/multiome/MOFA_multiome_integMACS_SCT.RDS")
```

Plotting

```{r}
# variance explained
plot_variance_explained(mofa_macs, plot_total = TRUE)

samples_metadata(mofa_macs) = mome_macs_SCT@meta.data %>%
  tibble::rownames_to_column("sample") %>%
  as.data.table

# correlated technical variables
correlate_factors_with_covariates(mofa_macs, 
                                  covariates = c("nFeature_RNA","nFeature_ATAC",
                                                 "nCount_RNA","nCount_ATAC", "animal"))

# example factors
plot_factor(mofa_macs, factors=1, group_by = "animal", color_by = "animal") 
plot_factor(mofa_macs, factors=10, group_by = "animal", color_by = "animal")

# example factor weights
plot_weights(mofa_macs, view = "RNA", factors = 1, nfeatures = 20, text_size = 4)

# select factors and run UMAP
factors = 1:get_dimensions(mofa)[["K"]]
factors = factors[c(3:6, 8, 11:get_dimensions(mofa_macs)[["K"]])]
mofa_macs = run_umap(mofa_macs, factors = factors, n_neighbors = 15,  min_dist = 0.30)

# plots
plot_dimred(mofa_macs, method = "UMAP", color_by = "animal", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "nCount_RNA", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "seurat_clusters", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)

# markers
plot_dimred(mofa_macs, method = "UMAP", color_by = "GAD2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "SLC17A6", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "MEIS2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "MEX3A", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
plot_dimred(mofa_macs, method = "UMAP", color_by = "GLI2", label = TRUE, stroke=0.05, 
            dot_size = 1, legend = T)
```

Add MOFA projection multiome data

```{r}
mome_atac_SCT_mofa = add_mofa_factors_to_seurat(mofa, mome_atac_SCT, views = "all", factors = "all")
DefaultAssay(mome_atac_SCT_mofa) = "RNA"
mome_atac_SCT_mofa@reductions$MOFA@assay.used = "RNA"
mome_atac_SCT_mofa@reductions$MOFAUMAP@assay.used = "RNA"

mome_macs_SCT_mofa = add_mofa_factors_to_seurat(mofa_macs, mome_macs_SCT, views = "all", factors = "all")
DefaultAssay(mome_macs_SCT_mofa) = "RNA"
mome_macs_SCT_mofa@reductions$MOFA@assay.used = "RNA"
mome_macs_SCT_mofa@reductions$MOFAUMAP@assay.used = "RNA"
```

Save data

```{r}
saveRDS(mome_atac_SCT_mofa, "data/processed/multiome/multiome_integATAC_SCT_annot.RDS")
saveRDS(mome_macs_SCT_mofa, "data/processed/multiome/multiome_integMACS_SCT_annot.RDS")
```






